Lifting the Fog: Image Restoration
Deconvolution is a data processing technique that is
very widely used in science and engineering. Biologists
first applied this method to fluorescence microscopy in
the early 1980s (Agard, 1984), but it was already in
common use by astronomers for sharpening images
acquired using telescopes. The term deconvolution is
derived from the fact that it reverses the distortion
(convolution) of data introduced by a recording
system, such as a microscope when it forms an image
of the specimen. Any microscope image of a fluorescent
specimen can, in principle, be deconvolved after acquisition
in order to improve contrast and resolution. The
most common application in biology is for deblurring
images acquired as three-dimensional (3D) image
stacks using a wide-field fluorescence microscope,
where each image includes considerable out-of-focus
light or blur originating from regions of the specimen
above and below the plane of focus. In essence, deconvolution
techniques use information describing how
the microscope produces a blurred image in the first
place as the basis for a mathematical transformation
that refocuses or sharpens the image. Deconvolution
of wide-field data can provide results resembling the
blur-free "optical sectioning" achieved with confocal
microscopes, which remove the out-of-focus light optically
before image acquisition by placing a pinhole in
the appropriate position in the light path. Deconvolution
is also applicable to confocal or multiphoton
images as they too involve a convolution of data and,
in practice, rarely exclude all out-of-focus information.
The best deconvolution techniques attempt to correctly
reassign each photon of the out-of-focus light from the
blurred optical sections being processed to their points
of origin within the image state. When successful, this
gives wide-field microscopy an advantage over confocal
optics, particularly with live-cell work, as all the
available emitted fluorescent light, including that
coming from out of focus regions, is collected in parallel
and is then reassigned to its correct location in the
3D image. This affords the wide-field microscope its
faster imaging speeds and flexibility. In contrast, the
out-of-focus fluorescence blur removed optically by
confocal microscopy can never be recovered, resulting
in lower light capture efficiency. However, deconvolution
requires powerful computers, thus introducing
additional cost and delay before the sharpened image
can be viewed. Nevertheless, if one is required to
resolve and analyse fine structural details and carry out
true volume analysis, deconvolution is an option that
should be seriously considered.
This technical guide is intended both for those
researchers who are completely new to the method
and for those who use deconvolution, but would like
a better appreciation of how the method works and the
present state of the art. We will first briefly explain the
principles of deconvolution and then describe the different
types of deconvolution methods currently available.
Next we discuss practical considerations and
limitations in applying the techniques. Finally, we
describe improved deconvolution algorithms, which
are currently being developed.
II. CONVOLUTION AND
The magnified image of a specimen observed by eye
or a digital camera mounted on a microscope is not a perfect representation of the specimen. The image is
blurred or convolved
by the microscope optics and
some information is lost (Fig. 1A). This is an inevitable
consequence of how objective lenses work and there
are no simple ways round the problem (Hecht, 2002).
The nature of image convolution in fluorescence
microscopy is appreciated most easily by viewing
small spherical fluorescent beads (with diameters in
the order of hundreds of nanometers; Figs. 1B-1D).
When the beads used are smaller than the smallest
detail that the optics can resolve (i.e., subresolution,
50 to 200nm), they form effective single point sources
of light and the image resulting from a single bead is
known as the point spread function (PSF)
, being a
record of how much the microscope has spread or
blurred a single point while imaging it (Jonkman et al.
2003; Figs. 1B-1D). Such images consist of a series of
concentric rings in three dimensions (so-called airy
) of out-of-focus light around points of light (Figs.
1B and 2A). The PSF is a three-dimensional function,
describing how the image of a subresolution point is
blurred in the z dimension as well as in x and y, and it
varies between different objectives, those with higher
numerical apertures giving less blurring (Figs. 1B-1D).
Wide-field, confocal, and multiphoton imaging configurations
all have their own characteristic PSFs. A biological
specimen may be thought of as a collection of
subresolution points, each of which is convolved by
the objective's PSF independently. Mathematically,
convolution involves replacing each point in the specimen
by a point spread function during the process of
image formation. It is the combination of out-of-focus light from these overlapping PSFs in and beyond the
plane of focus that leads to the familiar blur in widefield
fluorescence images (Figs. 3A and 4A).
|FIGURE 1 Convolution, deconvolution, and the point spread function (PSF). (A) Interpretive diagram of
the principles of convolution and deconvolution based upon a simple spherical object. (B-D) Wide-field fluorescence
PSFs obtained by imaging 100-nm fluorescent beads (excitation 520nm; emission 617nm) with a
×40/NA 0.85 dry objective and with a ×40/NA 1.35 oil immersion with adjustable collar set at NA 1.35 and
NA 0.85. Images shown are single median x, z planes from 3D data sets, step size 0.1 µm. (Inset) An x, y view
20 µm from the centre. Deterioration of the PSF from C to D is a consequence of mismatch of the immersion
oil refractive index with the reduced numerical aperture set. Resolution in z depends upon both RI and NA.
|FIGURE 2 (A) Three-dimensional (x, y, z) view of a wide-field fluorescence point spread function. For
clarity, the volume representation is shown as a negative image. (B) Two different representations of the
Fourier transform of a PSF-an optical transfer function (OTF). In the OTF, frequency information is plotted
in "k space" or reciprocal space (where kz is reciprocally related to z and kr to x and y in real space, see Technical
Summary 2). OTFs are often averaged radially over x, y, hence the axis kr. The area covered by the OTF
corresponds to the frequency information collected by the optics. The "missing cone" region (arrows) corresponds
to lost frequencies due to the limited size of the objective pupil, a consequence of this being the far
greater blurring of an object along the z axis than in x and y.
|FIGURE 3 Comparison of wide-field and confocal imaging of living Drosophila egg chambers (stage 8)
expressing Tau-GFP localised to the tubulin cytoskeleton. (A) Wide-field image of the area surrounding the
oocyte nucleus (see insert: DIC image of the egg chamber). The image required 200ms exposure. (B) Image
A after 3D iterative deconvolution using the Applied Precision Resolve 3D algorithm (softWoRx, API). (C) Point
scanning confocal optical section of a similar egg chamber, which took 2 s to collect. (D) Wider view of the
confocal image of the egg chamber from C. All images were collected with ×60/1.2 NA water immersion
Scale bars: 100µm (A-C) and 50µm (D). The oocyte nucleus (n) is indicated in each case.
|FIGURE 4 Comparing the results of different deconvolution methods on a challenging 3D (60 image)
wide-field data set of a Drosophila egg chamber (z-step 0.2µm, x, y pixel size 0.212µm; a ×60 1.2 NA water
(A) Single unprocessed wide-field image. (B) The corresponding image plane after 3D iterative,
constrained deconvolution of the data set (softWoRx, API: empirical PSF). (C) Deconvolution as in B using a
second 60 image 3D data set of the same tissue collected at an x, y pixel size of 0.106gm--higher sampling
rate. (D) Two-dimensional iterative constrained deconvolution of the first image data set (softWoRx, API:
empirical PSF). Each z plane is deconvolved independently as if it were part of a time-series data set.
(E) Deconvolution as in B but using a different PSF (OTF) and more severe smoothing. (F) Using the "nearest
neighbours" deconvolution algorithm (softWoRx, API). (G) Using the 3D iterative, "adaptive blind" deconvolution
algorithm (Autodeblur, Autoquant-10 iterations; low noise; no spherical aberration; recommended
expert settings). (H) Using the 2D iterative, adaptive blind deconvolution algorithm (Autodeblur, Autoquant-15 iterations; low noise; no spherical aberration; recommended expert settings). Scale as for Fig. 3D. Raw data
were 12 bit saved as 16 bit; the images shown are 8 bit; with contrast stretched to fill the full dynamic range.
refers to a mathematical
process by which the effects of image convolution by
the microscope are reduced or reversed to allow one to reproduce more closely the detail present in the
original specimen (Agard, 1984; Hiraoka et at., 1990;
Figs. 3B and 4B-4H). The more advanced deconvolution
methods attempt to reassign this out-of-focus light
to its point of origin (i.e., converting airy rings back
into single point sources of light); the resulting image should be sharper with increased contrast and under certain circumstances Z resolution (Figs. 3B, 4B, 4C, 4E and 4G; Agard, 1984; Agard et al.
III. DECONVOLUTION, DEBLURRING, AND IMAGE RESTORATION
Given the raw image and the PSF for the imaging system it should, in principle, be
possible to reverse the convolution of an object by the microscope mathematically,
according to a simple relationship-the so-called "linear inverse filter" (Technical
Summary 1). However, in practice, simple reversal of convolution is not possible,
primarily because of the confounding effect of noise in image data, which becomes
amplified during this process (Technical Summary 1). Several fundamentally different
deconvolution approaches of varying complexity have been developed to overcome this
problem (see Table I; Agard, 1984; Agard et al.
, 1989; Holmes et al.
, 1995; van Kempen et al.
For the biologist, it is important to be able to recognise the different algorithms and appreciate the consequences of the those differences to processed data (noted in Table I). The differences are significant: from simple rapid contrast enhancement by deblurring algorithms such as "nearest neighbours" to the much slower restoration procedures such as the constrained iterative algorithms, which can, under ideal conditions, "recover" information lost through convolution by the microscope optics (see later).
Technical Summary 1: Convolution Theory and Fourier Space
Convolution refers to the effect that the microscope optics has in forming the image of an object viewed through the microscope. the microscope image is the result of the convolution (symbolised by
) of the object by the PSF of the microscope (Fig. 1). Mathematically, convolution involves applying the function taht relates object to image to each point of the function defining the image (Hecht, 2002). The PSF empirically defines the function relating an object to its image:
|Microscope image = object PSF
In theory, determing what the object should look like, given the microscope image and the PSF, simply requires reversal of this convolution. Performing this calculation using image data directly (known as calculating in "real space
") is computationally very time-consuming. This limitation may be overcome by carrying out calculation in "Fourier space
" by computing the Fourier transform (FT) of both image data and the PSF (see Technical Summary 2). The Fourier-transformed PSF is also known as an optical transfer function (OTF
). In Fourier space, the convolution relationship described earlier becomes a simple multiplication:
|FT (microscope image) = FT (object) × FT (PSF), or equivalently
|FT (microscope image) = FT (object) × OTF (Van der Voort and Hell)
Deconvolution on the basis of the aforementioned equation is known as a simple linear inverse filter (Table I). However, the Simple linear inverse filter is of very little practical use, as the relationship between image and object is invalidated by noise
in the collected image. The simple linear inverse filter does, however, provide the theoretical basis for more complex deconvolution algorithms that take account of real imaging situation (Hiraoka et al.
A. Classification of Algorithms
There is considerable confusion caused by the existence of different names for deconvolution algorithms due to the history of development of this method (Agard, 1989; van Kempen et al.
, 1997). Currently, only a few algorithms are routinely employed in biological image processing (Table I). In an article by Wallance et al.
(2001), algorithms are classified info (1) "deblurring
" methods (Table I), which are said to act two-dimensionally, on one z
plane at a time, and (2) "image restoration" or "true deconvolution" methods, which act in a truly three-dimensional way on all planes simultaneously. IN general, deblurring methods may be useful for a rapid qualitative improvement in image contrast, e.g., to aid data screening (Fig. 3F, nearest neighbour deconvolution). However, the most powerful algorithms are the constrained iterative procedures of the second class:considered as the method of choice for quality 3D data processing, these will be the focus of the remainder of this article. Within the family of constrained iterative approaches, different algorithms vary in the way noise is modelled and how PSF information is dealt with (Table I; Agard et al.
, 1989; Holmes et al.
, 1995; van Kempen et al.
B. Constrained Iterative Deconvolution
|TABLE I Deconvolution Algorithms Used in Biological Fluorescence Image Processing a
||Algorithm/product name b
|Widely available in image
processing software as
"nearest neighbours" or "no
(e.g., Metamorph, Universal
|Works on the basis that most of the out-of-focus blur originates in the
neighbouring planes in z (when sampling at approximately the z resolution). The nearest z sections above and below the section being
corrected are blurred with a filter and these blurred images are then
subtracted from the plane of interest to give the deblurred section. "No
neighbours" extrapolates only from a single plane.
- Computationally inexpensive and therefore useful for quick results
- Prone to creating artefacts
- Signal is reduced by the subtractions and noise is propagated
- Affects quantification: should be used only for qualitative contrast
|Image restoration class
Linear inverse filter
|For example, the Tikhonov-
Miller filter used in
|The simplistic approach of reversing convolution by the optics. Does not
take account of the effects of noise. Often used as the "first guess" for
iterative deconvolution. Still a true 3D method requiring a PSF, which
attempts to reassign out of focus information to its point of origin.
- Computationally inexpensive and therefore useful for quick results
- Prone to creating artefacts
- Noise is propagated, thus tends to be used only as a qualitative method
- Not capable of super resolution but can restore information supported
by the OTF coverage.
|For example, the regularised
Wiener filter. Found in
several applications, e.g.,
filter from Autoquant and
Axio Vision 3D-Deconvolution
from Carl Zeiss
|A refinement of the above employing regularisation so that the
deconvolution solutions are physically realistic and artifacts are
suppressed. Regularisation involves limiting the possible solutions that
are accepted in accordance with certain assumptions made a priori about
the object, such as the degree of smoothness, essential when dealing with
real, noisy data. Limiting the contribution of noise by regularisation
comes at the expense of resolution, and to optimise this trade-off between
image sharpness and noise amplification, different values of the regularisation parameter must be assessed.
|For example, the modified
Jansson van Cittert algorithm
(Agard et al., 1989)
implemented in modified
form in soflWoRx from API
|A further refinement on the aforementioned; an additional positivity
constraint is applied that restricts output image intensities to be positive
(see main text).
- Computationally expensive and therefore slow
- Less prone to artifacts
- Signal is reassigned, increasing the brightness of features and the
- Works very well when the OTF is matched to data, suffers when data
is affected by spherical aberration
- Quantitative and capable of superresolution in z (beyond the z optical
resolution limit of the microscope)
(Poisson noise model)
|For example, the Richardson-
Lucy algorithm implemented
in Huygens Essential from
Scientific Volume Imaging.
Also AxioVision 3D-
|A variant of the constrained, iterative algorithms that assumes a Poisson
model to quantify the noise present during imaging. Huygens essential
uses the classical maximum likelihood estimation method and either
optimised empirical or theoretical PSFs.
- Computationally even more expensive, requiring more iterations and
- More recent than the Agard/Sedat-modified Jansson van Cittert
- Quantitative (but see main text) and capable of superresolution in Z
- Useful for noisy, photon-limited confocal imaging data (e.g., van
Kempen et al., 1997)
|Includes the EM-MLE
algorithm) of Autoquant's
Autodeblur-2D and 3D blind
|Blind deconvolution is also iterative and constrained but it determines an
estimate of both the object and the PSF of the imaging system based upon
raw image data and certain a priori assumptions about the PSF. These
algorithms also assume a Poisson model of noise in the image.
- Determining two unknowns is computationally more expensive and
therefore even slower
- Does not require an empirical PSF and is more tolerant of spherical
aberration and asymmetry
- Can generate a derived calculated PSF that can be used to speed up
- May not be able to restore to as high resolution as when using an
optimised empirical PSF
- Better at dealing with signal-limited noisy data (such as confocal images)
- May affect image data such that it is no longer absolutely quantitative
|a Represents a summary of commonly used algorithms and is not intended to be exhaustive.
|b These products may be sold (under license) by other agents.
|c National Institute of Health Image-J software is free to download from http://rsb.info.nih.gov/ij.
The constrained iterative deconvolution approach (Agard, 1984; Agard et al.
, 1989) is truly restorative, in that it attempts to reassign (put back) the out-of-focus light to its expected point of origin rather than simply subtract it from the imge as with the deblurring methods (Table I; Figs. 3 and 4). This not only increases the brightness of features in the object thus improving signal-to-noise levels, but also improves contrast and apparent resolution. Furthermore, these algorithms are capable, under ideal circumstances, of deconvolution solutions with so-called superresolution
along the Z
axis-the recovery of detail lost from the image by the convolution of the optics (i.e., beyond diffraction-limited resolution). This phenomenon is usually explained in terms of "the recovery of lost spatial frequencies" or the "missing cone
" of z
information (Hiraoka et al.
, 1990; Goodman, 1996), a reference to the shape of the PSF in Fourier space
(Fig. 2B; Technical Summary 2).
Technical Summary 2: Fourier Transformation and Fourier Space
Fourirer transformation represents an image in terms of the spatial frwquency information it contains. sharp edges or fine details correspond to high spatial frequencies; large areas of slowly changing intensity have low spatial frequencies. The entire image can be considered to be the result of the combination of a set of complex "waves." The summation of these waves to create the image is described mathematically as the three-dimensional integral of many simple sinusoidal wave functions, each having specific frequencies, phases, and amplitudes. The Fourier transform of the image (a "discrete Fourier transforms" in the case of image data) is a plot of the frequency information present in the real space image, each point representing a particular spatial frequency (see figure below; Hecht, 2002).
|* Note the two different ways that the Fourier space image can be representated depending upon where the origin is located (see also Fig. 2B). The lack of complete radial symmetry in the discrete Fourier transform of the 2D image is an artefact of the discrete Fourier transform algorithm used (Image-J, FFT; NIH).
- Fourier transformation is used in deconvolution processing simply because the convoluation calculation is much faster in Fourier space (Frequencyx, Frequencyy, Frequencyz) than in real space x,y,z (Van der Voort and Hell, 1997).
- In Fourier space, the dimensions along the axes are spatial frequency. These reciprocal space coordinates do not map directly to x,y,z positions in real space, but are related to them reciprocally, i.e., things that are closer together in real space (fine details) give high spatial frequency componentsthat are further from the mathematical origin of the Fourier transform in Fourier space and vice versa. For this reason, Fourier space is often reffered to as "reciprocal space." Amplitudes in a real space image result from the summed amplitudes of each spatial frequency component (effectively how often a particular frequency occurs).
The name used for the "constrained iterative" class of deconvolution refers to the two critical features that define it: "constrained" and "itenative." The term "constrained"
refers to the use of the "Positivity constraint." "Iterative"
refers to the way which these algorithms are implemented by repeated cycles of comparison and revision.
The Positivity constraint is simply a mathematical tems in the algorithm that rejects solutions for the image intensity at any point that give negative values. This limits possible solutions to more realistic out-comes-photons are there or not there, but are never negative. The positivity constraint means that there is no longer a simple linear relationship between the collected image and the deconvolved result, as with the inverse linear filter. Such algorithms are, therefore, also known as nonlinear methods. [The positivity constraint is similar in concept to regularisation (Table I), which also imposes constraints on possible solutions, but should not be confused with it.] A consequence of the positivity constraint is that it confers the superresolution capabilities of these algorithms by allowing meaningful extrapolation to "recover" some of the z
information missing from raw data due to the convolution by the PSF of the microscope (Hiraoka et al.
, 1990; Goodman, 1996).
The iterative process of these deconvolution algorithms may be described in simplified empirical terms as follows. An "initial guess"
at the deconvolved solution (i.e., the true object) is generated from raw image data. This initial guess
can be different depending upon the actual algorithm used and may be raw image data transformed with a fast, simple, linear inverse filter step (see Table I) or simply raw image data itself after smoothing. The next step assesses the success of the initial guess as an estimate of the true object by comparing the guess convolved with the PSF of the microscope with the raw original image data. If the guess is correct, then convolving it according to the expected related relationship between the true image and collected image (on the basis of the appropriate PSF, see Technical summary 1) should yield values equivalent to raw image data, i.e., the difference between the two should be minimal. Iterative processing then begins: repeated cycles of calculating a revised convolved guess and comparing with original data operated to incrementally minimise the difference between the two. The positivity constraint is imposed at each revision of the guess. In practice, the number of iterations is predetermined either as a definite number of cycles (which can be from tens to hundreds depending upon data and algorithm, Table I) or when a predefined limit is reached in terms of the difference found upon comparison. Generallyy, excessive numbers of cycles are nonproductive and an optimum can be found for one's particular data type, this is commonly between 10 and 40. The final guess is taken to be the optimally deconvolved image.
A consequence of all this processing is that implementation is very computationally demanding and so potentially slow. However, modern computers have become so fast and relatively inexpensive that a large deconvolution task, such as a stack of 64 z
sections, each of 512 × 512 pixels, can be completed in a few minutes.
C. Using Deconvolution
To achieve the best image quality from a wide field imaging system (i.e., resulting in features sharply defined by high contrast and with a resolution is almost indispensable.
Wide-fiels deconvolution is appropriate to many imaging applications. However, it works best for 3D image sets obtained from thin (>50 µm) culture cells or optically very transparent material with low background fluorescence. These criteria are most usually met with fixed material. Live cell imaging is far more challenging for deconvolution, as it introduces problems such as motion blur and increased scattering, bleaching, and spherical aberration
(SA) made worse by the constraints on excitation exposure due to phototoxicity. Deconvolution can be used to assist visualization of fine structures (Figs. 3 and 4), for calculating z
projections (extended focus images), for 3D rendering and quantitative volume analysis, for colocalisation studies (Landmann, 2002), and for fast quantitative tracking of dynamic structures (MacDougall et al.
Wide-field deconvolution can provide a similar degree of reduction in out-of-focus blur as does confocal microscopy, but without the elimination and loss of signal associated with passing the emitted light through a confocal pinhole (Swedlow et al.
, 2002; pawley and czymmek, 1997). Aided by the high quantum efficiency (QE) charge-coupled device (CCD) detectors used (Amos, 2000), wide field is both fast and sensitive compared to point scanning confocal imaging, making it especially useful for rapid imaging of labile, dynamic processes in live material (Periasamy et al.
, 1999; Wallace et al.
, 2001; Swedlow et al.
Confocal microscopes, however (particularly in the special case of multiphoton imaging), are better at imaging thick, opaque, and scattering specimens with higher background fluorescence (Periasamy et al.
, 1999; Diaspro, 2001; swedlow et al.
, 2002). Confocal systems also tend to have simultaneous
multichannel imaging, which is not common for wide
field. On the down side, confocals are usually photon
limited and so can only be used effectively with
samples that are bright enough to see clearly by eye
and that have a narrow dynamic range of signal
strength, as dim samples require intense illumination
(which promotes bleaching) and high photomultiplier
(PMT) gain settings (which increase noise). Point scanning
confocals also tend to be slow, which means that
they suffer from motion blur artefacts when imaging
dynamic processes in living cells.
Deconvolution and confocal imaging are by no
means mutually exclusive: confocal imaging can
nearly always benefit from the improvements in contrast,
signal-to-noise ratio, and resolution afforded by
restorative deconvolution methods (Wiegand et al.
2003). Furthermore, while excluded photons cannot be
restored, the application of deconvolution to confocal
images does allow the confocal pinhole to be opened
slightly during imaging, sacrificing confocality but
increasing signal and reducing spherical aberration
effects (Pawley, 1995). The brighter but more blurry
images are then restored by deconvolution. This is particularly
helpful where SA precludes the use of small
pinhole settings, e.g., when imaging deep within a
specimen (>10µm). However, one of the biggest limitations
when deconvolving confocal images is that
they tend to be very noisy because of the low signal
available due to the low light throughput and low
quantum efficiency of photon multipliers used as
detectors in confocal imaging. This problem can best
be dealt with either by averaging frames or decreasing
scan rate, both at the expense of speed, rather than by
increasing the gain on the PMT (which merely introduces
more noise). The Huygens Professional
from scientific volume imaging (SVI) addresses the
noise content of raw data and makes use of this information
during restoration. Another potential problem
with deconvolution of laser scanning confocal images
is that the pinhole size affects the point spread function.
deconvolution software or Autoquant's Autodeblur
program both offer solutions to this
problem in that they have options to take account of
the effect of the confocal pinhole on the PSF.
The issues of limited signal to noise and slow scan
speed of point scanning confocals means that the generation
of image data of sufficient quality to fully
benefit from deconvolution is not well suited to fast
live-cell imaging. A spinning disc (or "multifocal")
confocal microscope, however, uses multiple pinholes
for high-speed scanning and captures images on a
highly sensitive CCD camera, which is inherently much less noisy than a PMT, combining the advantages
of increased contrast of confocal imaging with
the efficiency of the CCD detector and speed of wide
(SVI) provides a deconvolution algorithm
specifically tailored to the multiple scanning
spots of this form of imaging.
While true deconvolution is a spatial 3D (x, y, z
phenomenon, often the need is to process tme-lapse
data (x, y,
). Algorithms exist that are specifically
designed to deal with such data (see later for
details); however, deconvolution with only spatial 2D
data is necessarily not as effective as with a full 3D
stack of z sections, as it lacks information to take
proper account of distortion in the z
dimension. It is
also important to note that deconvolution cannot take
account of "motion blur" during image capture so suitably
short exposure times are essential.
IV. PRACTICAL IMPLEMENTATION
Deconvolution, particularly the more advanced
approaches, is implemented through a software
package where the algorithm is generally assisted by
data correction before and noise reduction steps both
before and between iterations. With a given deconvolution
package, the quality of results obtained will
depend foremost upon the quality of raw image data
and upon the accuracy of PSF data. Next in importance
is the algorithm used and how well it is implemented:
noise handling, background or dark signal correction,
the initial guess, the regularisation parameter, and filtering
and smoothing steps. As different settings,
algorithms, or packages may have varying success
with different data, it is invaluable to test different
programs to understand how a program behaves with
your own raw data (Fig. 4A-4H) and to pay close
attention to software manufacturers' criteria for raw
data and data collection.
A. Collection of Raw Image Data
The quality of raw data is arguably the most important
determinant in obtaining good deconvolution
results. Essentially, improving raw data involves
improving the useful
signal-to-noise ratio of images
(Pawley, 1995). The most important factors relating to
this are given in Technical Summary 3. In our experience,
it is always easier to meet criteria for good
imaging in fixed material than it is for living material where compromises must be made (Davis and parton, 2004).
In some situations, such as rapid time-lapse experiments with live material, it is desirable to collect 2D x,y
images rather than full 3D x,y,z
data sets at each times point. Some 3D iterative algorithms will permit limited processing of 2D data sets by effectively ignoring the z
dimension (e.g., Applied Precision's softWoRx;
Figs. 4D and 4H). In such cases, "displaced" out-of-focus light in the plane of imaging is effectively reassigned to its point of origin within the same plane, but out-of-focus light originating from other planes of focus is ignored. Accordingly, this works best when the out-of-focus blurring effect in z
is least detrimental, i.e., with low numerical aperture, low magnification objectives, or thin, optically transparent specimens. Autoquant's Autodeblur
program suite offers an iterative blind algorithm for use with 2D images that similarly tackles x,y
data on the assumption that everything is in one plane of focus, which is acceptable for thin specimens. This should not be confused with the no-neighbours algorithm (Table I), a simple deblurring method for 2D data, which is essentially a contrast enhancement algorithm.
Technical Summary 3: Data Collection Procedures
1. Use a correctly set up and aligned microscope with high-quality objective lenses, accurate z
control (by Piezo focusing or motorised stage, Fig. 5), and low noise detectors of good dynamic range and high QE (proportion of photons arriving at the detector that go to produce an output signal).
|FIGURE 5 Different approaches to z movement control. (A) An x,y,z motorised stage from the API Deltavision system. (B) A piezo z-drive nosepiece from Precision Instruments. Note that use of a nosepiece under an objective may alter its PSF.
The quality of deconvolution is dependent upon correct positional information. Pixel sizes should be determined by accurate calibration of the image in x
, and the z
drive should be as precise and reproducible as possible. [For example, the nanomovers offered by applied Precision Inc. (API) for their Deltnvision system have 4nm precision and 30nm repeatability. Pifoc drives supplied by Precision Instruments are more accurate and reproducible, depending on the speed of movement and time allowed to settle (Fig. 5), but alter the point spread function and can only be mounted on objection at a time.]
|FIGURE 6 Cargille immersion oils of varying refractive
index (RI) and several useful RI values. Note that dilutions
of glycerol (80 to 40%) give RI of 1.446 to 1.391.
In general, current deconvolution methods are poor at taking account of aberrations in the imaging optics or distortions produced by imaging in thick specimens (primarily SA) or by motion blur. Sample preparation using appropriate mounting media to match the refractive index (RI) of the specimen and the use of correct immersion medium for the objective used (dry, oil, water, or glycerol) can reduce spherical aberration. It is also important to use the correct coverslip thickness or correction collar settingfor the objective (default 0.17mm; #1.5) and to ensure that as far as possible the specimen is immediately adjacent to the coverslip. Imaging deep into specimens (>10 µm) increases SA, which can be peartly corrected using objectives with correction collars or by using immersion oils of higher than normal RI (Fig. 6; Davis, 2000).
2. To realise the optical resolution of your imaging system, it is important to follow the Nyquist
sampling rule by using a pixel size in the electronic imaging device at least two times smaller (ideally, a minimum 2.3 times) than the expected optical resolution in x,y,
(Pawley, 1995; Jonkman et al.
, 2003). It is possible to determine the theoretical optical resolution based upon the numerical aperture of the objective and emission wavelength using the equation below. The values obtained should be used as an estimate on which to base the digital sampling size. however, in reality it may benifit the final result to make a small sacrifice in the sampling rate by binning pixels on a CCD readout to boost signal-to-noise ratio, increase imaging speed, or avoid flurochome photobleaching. Figure 4C shows deconvolution result improvement with better x,y
spatial sampling (0.106 µm pixel) compared to fig. 4B (0.212 µm), despite the lower fluorescence signal.
Calculation to determine theoretical resolution limits:
|Lateral (XY) resolution = 0.61 * wavelengthemision/numerical apertureobjective
|Axial (Z) resolution = 2 * wavelengthem * refractive index/(numerical apertureobj)2
(refractive index in this case refers to the specimen immersion medium
3. In general, it is not advisable to collect Z planes so far below or above the best focus that only blur is seen, as you only end up bleaching the sample.
4. Noise is a significant problem for deconvolution and should be reduced during collection as much as possible. Noise is generally given as the signal-to-noise ratio (S/N; Sheppard, 1995). Poisson distributed shot noise
(S/N = n
or simply √n
, where n
may be approximated as the image signal) is the fundamental limitation in S/N. As the number of photons detected increases, the signal-to-noise ratio improves according to the poisson statistics of photon detection. The signal should not, of course, be so high as to saturate the detector. both the Huygens
(SVI) and Autodeblur
(Autoquant) programs make use of "roughness penalty factors," which, unlike smoothing filters, can be set by the user to constrain their deconvolved solutions to reduce noise while preserving edge features.
The detector should have good dynamic range, high quantum efficiency (QE), low dark signal, and low readout noise (Amos, 2000). When taking short exposures with CCD detectors, readout noise tends to be the most important source of detector noise, whereas when taking long exposure, dark current predominates.
Slow-scan cooled CCD detectors with low dark current and readout noise, reading out pixel intensities as 12-16 bit images (214
= 4096, 216
= 65,536 intensity levels), are used most commonly in wide-field fluorescence imaging systems. They can produce images with a large dynamic range (total intensity levels/intensity levels of noise) and favourable overall S/N ratio.
Confocal systems are less light efficient, and the images they produce are generally photon limited and most commonly recorded as only 8 bit data (=256 intensity levels). Therefore confocals are more prone to noise problems. Point scanning confocals use relatively low QE photomultipliers, whereas disc scanning systems use CCDs. Imaging only bright signals or averaging several frames to count more photons can help reduce such noise problems.
5. Generally a dark signal
Should be determined (by collecting an image without illumination). However, as different algorithms deal with this parameter differently, which can have significant effects on deconvolution, its use requires careful consideration. In certain algorithms, if too little subtraction correction is made the positivity constraint is not implemented correctly, too much and genuine signal is lost. Huygens
Professional (SVI) permits automatic estimation or manual input of this information. Some programs correct for background
signal from part of an image that does not include the specimen (but which can, however, include certain sources of autofluorescence
from slide preparations and evenly distributed non-specific staining
6. Intensity fluctuations due to light source flicker or bleaching during the collection of z
sections or time series images have an adverse affect on deconvolution. Using stable power suppliers, regulatly changing mercury arc lamps, or measuring fluctuations and correcting collected data can help significantly (Delta Vision
System, API). Bleaching may be reduced by the use of an antifade reagent, which are available commercially or can be home made. Some algorithms include bleaching correction based upon an expected theoretical rate of decline in fluorescence.
B. The PSF (and Its Fourier Transform the OTF)
In addition to optimising raw image quality, using the correct PSF for the image being deconvolved is fundamental to the final result, as one might expect from the convolution relationship discussed in detail earlier (Fig. 1; 4B compared to 4E). A PSF
may be emoirical (i.e., measured; Hiraoka et al.
, 1990 but see Van der Voort and Strasters, 1995; Davis, 2000) or theoretical (calculated). The former is generally recorded by 3D imaging of subresolution beads using exactly the same optics as used to image the specimen, whereas the latter is generated by the software package used, after inputting the emission wave-length, objective lens details, and refractive index of the immersion medium and mounting medium (e.g., Huygens from SVI; Zeiss 3D Deconvolution Module from Zeiss; Autodeblur from Autoquant). Important considerations relating to PSFs are given in lechnical Summary 4.
Technical Summary 4: Use of PSF (OTF)
- if collected correctly, these have the greatest potential for high-quality deconvolution of images taken with high numerical aperture ienses. However, a poor empirical PSF is certainly worse than a theoretical one (Hiraoka et al., 1990).
- Are often collected at 488-nm excitation and 520-nm emission wavelengths. Software packages usually make adjustments when the actual imaging situation is at a different wavelength. Huygens (SVI) offers the possibility of multiwavelength PSF Processing as an aid to chromatic aberration correction in colocalisation.
- Experience reveals that collecting a PSF to match the true aberrated imaging situation in vivo (e.g., by injecting fluorescent beads into a cell or by mounting beads so that they are imaged through a particular depth of media of refractive index close to that of a cell) is less effective than using a PSF corresponding to optimal imaging conditiond and aspiring to match in vivo imaging conditions to that situation.
- Some software packages come with prerecorded optimised (e.g., averaged over many beads or "processed" by averaging, smoothing, or deconvolving) empirical OTF's for particular objectives, and using these is often preferable to collecting your own (e.g., API's softWoRx package; Fig. 2B). Huygens (SVI) permits optimisation of your own empirical PSF data by registering and averaging images of multiple beads and correcting for the fact that a bead is, in reality, not an infinitely small point source.
- In the case of objective lenses with numerical aperture collars, it may be necessary to collect specific PSFs for each setting of the collar (1C and 1D), similarly for different confocal pinhole apertures.
Blind Deconvolution, Estimated, or Adaptive PSF
- These work well with low NA, low magnification objectives.
- They are usefully employed in some software packages for use with confocal (and MP) imaging, as they can be optimised for different confocal pinhole settings by calculation.
- "Blind deconvolution," Autoquant, Autideblur) offer an effective way to circumvent some of the issue mentioned earlier. Insted of using an empirical or calculated PSF, an estimate of the "true" PSF is generated iteratively from the image data itself at the same time as image restoration (Holmes et al., 1995).
- Adaptive blind deconvolution can be useful where a representative PSF is not easy to obtain, such as with confocal imaging using variable pinhole settings, multiphoton imaging, or imaging in the presence of SA.
- Adaptive blind deconvolution is not a perfect solution, as it is only an estimate determined on the basis of severe constraints imposed upon possible solutions.
Ultimately, it is necessary to determine by trial and error the approach that works best for any particular data type. In virtually all cases, the use of a signal PSF for an entire 3D image set is a compromise, especially with live material. Generally, the optical conditions vary between parts of the same image or at different z planes (i.e., the PSF itself varies spatially). However, most current algorithms assume a spatially invariant PSF to simplify processing (Hiraoka et al.
C.Computer System Organisation
Advances in computer technology have had a great impact upon deconvolution, largely because of the computational demands of this approach (Holmes et al.
, 1995; Wallace et al.
, 2001). Be aware that with the requirements of deconvolution for multiple z
planes and with CCD image detection noe able to generate 1024 × 1024 × 16 bit images many times a second, computer systems must deal with many gigabytes of information within relatively short periods of time. Implementation of deconvolution algorithms requires appropriate processor power and adequate data transfer, short-term storage, and archiving capabilities While in the past deconvolution required special high-performance workstations, dual processor personal computers with at least 1Gb of RAM, greater than 50Gb hard drives, CD, or DVD-R writers and network
connection speeds of 100Mbit/s to 1Gbit/s are
now standardly used for deconvolution. To increase
the efficiency of a laboratory, it is desirable to have
separate computers dedicated to image capture and
image analysis. A high-quality properly set up monitor
(24- or 32-bit colour; calibrated to set contrast and
brightness) is necessary for viewing image data.
Where many users are involved, it is desirable to have
a rack-mounted cluster of many computers linked to a
RAID disk (redundant array of independent discs),
networked to stand-alone acquisition and processing
stations for individual user access.
V. ASSESSMENT OF
Deconvolution works best when imaging conditions
are near optimal, which is possible for fixed
material but rarely achieved in living specimens (Kam et al.
, 2000). If the raw images are poor, deconvolution
can perform poorly or even produce artefacts that
degrade the images. Visual assessment of the removal
of out-of-focus blur is a useful guide of success, taking
care to correctly set the brightness and contrast of the
image display. Features should emerge sharpened
with increased brightness after processing. However,
as a general guide, you should only trust features
observed in the deconvolved image when they are also
present, however faintly, in the unprocessed original
image. Comparison of the deconvolved results with
images acquired by an independent technique, such as
electron microscopy or confocal imaging, is also
useful. Detailing deconvolution artefacts and their
basis is beyond the scope of this article and are dealt
with elsewhere (e.g., Wallace et al.
, 2001). In general,
small punctate or ring-like features should be viewed
with scepticism, while excessive noise suggests poor
initial data or problems with the parameters used. If
detail appears to be lost after processing, then initial
background subtraction may have been too high or filtering
and noise suppression steps too aggressive.
signal and permit or even improve quantitative
analysis of data (van Kempen et al.
Wiegand et al.
, 2003). However, some argue that the
reassignment of distant, dim light where noise dominates
(Pawley and Czymmek, 1997) and also the processing
by which noise is dealt with [particularly in
the case of blind deconvolution (Van der Voort and
Strasters, 1995)] result in absolute conservation being
compromised. If strict quantitative intensity comparison
(or intensity ratio calculation) is required, undeconvolved
data should always be analysed as well
for comparison and independent controls sought. As
with all image analysis, caution should be exercised
when making direct comparisons between different
imagesminitial imaging conditions and deconvolution
processing should be comparable between data sets.
Quantitative positional and structural analysis,
which is not so dependent upon absolute intensity
values, including centroid determination, movement
tracking, volume analysis, and colocalisation, have all
been performed successfully on deconvolved data
(e.g., Van Der Voort and Strasters, 1995; Wallace et al.
2001; Landmann, 2002; Wiegand et al.
Macdougall et al.
, 2003). Where quantitative analysis
is intended, it is essential to apply deconvolution
systematically, with an understanding of possible
artefacts and, if possible, with confirmation by an
VII. FUTURE DEVELOPMENTS
Improvement in the quality of the initial imaging is
the most effective way to enhance deconvolution. For
example, correct sampling, noise reduction, use of
photon limited (rather than noise limited) detectors,
precise and rapid piezo z movers, and better corrected
objectives all help to improve the quality of imaging
and hence the deconvolution result. However,
advances are also being made in the quality of
processing. Faster computers are allowing the use of
increasingly more complex calculations within realistic
timescales. More advanced deconvolution algorithms
are being developed to deal with the poor
signal to noise and with the aberrations associated
with imaging in vivo
(Holmes et al.
, 1995; Kam et al.
2000). Pupil functions
, which are in use in astronomy,
are beginning to be used in biology (Hanser et al.
in order to correct for asymmetries in the PSF (current
deconvolution algorithms assume that the PSF is radially
symmetric in x
and bilaterally symmetric in z
). Iterative constrained algorithms aided by wavelet
may allow us to tackle situations of poor signal to noise and distorted PSF when imaging deep in vivo
using confocal and multiphoton techniques
(Boutet de Monvel, 2001; Gonzalez and Woods, 2002).
We thank John Sedat, Satoru Uzawa, Sebastian
Haase, Lukman Winito, and Lin Shao (University
of California San Francisco) and also Zvi Kam
(Weizmann Institute of Science) for interesting discussions
relating to deconvolution. We are also indebted
to Jochen Arlt and the COSMIC imaging facility
(Physics Department, University of Edinburgh) for
invaluable technical advice and access to advanced
imaging equipment and also to Simon Bullock, David
Ish-Horowicz, Daniel Zicha, and Alastair Nicol
(Cancer Research-UK, London) for allowing access to
their imaging equipment and for the introduction
to wavelet analysis. Thanks for technical advice
must also go to the many contributors to the Confocal
List (URL: http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal); Carl Brown of Applied Precision Inc.
(USA); Jonathan Girroir of AutoQuant Imaging (USA);
and Rory R. Duncan, University of Edinburgh Medical
School. We are grateful to Renald Delanoue, Veronique
van De Bor, and Sabine Fischer-Parton (University of
Edinburgh) for critical reading of the manuscript.
Useful Web Sites
Andor Technology (EMCCD cameras)
Applied Precision (Deltavision) www.api.com/lifescience/dv-technology.html
Autoquant (see education pages)
www.aqi.com also www.leica.com
Carl Zeiss (3D Deconvolution) www.zeiss.co.uk
(Axio Vision, 3D-Deconvolution
Huygens (see essential user guide) www.svi.nl also
www.bitplane.com and www.vaytek.com
Image J (FFTJ and Deconvolution-J)
Mathematica for wavelet analysis www.hallogram.com.mathematica.waveletexplorer/www.wolfram.com/products/ applications/wavelet /
Power Microscope On-line deconvolution
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