 Deconvolution

In mathematics, deconvolution is an algorithmbased process used to reverse the effects of convolution on recorded data.^{[1]} The concept of deconvolution is widely used in the techniques of signal processing and image processing. Because these techniques are in turn widely used in many scientific and engineering disciplines, deconvolution finds many applications.
In general, the object of deconvolution is to find the solution of a convolution equation of the form:
Usually, h is some recorded signal, and ƒ is some signal that we wish to recover, but has been convolved with some other signal g before we recorded it. The function g might represent the transfer function of an instrument or a driving force that was applied to a physical system. If we know g, or at least know the form of g, then we can perform deterministic deconvolution. However, if we do not know g in advance, then we need to estimate it. This is most often done using methods of statistical estimation^{[citation needed]}.
In physical measurements, the situation is usually closer to
In this case ε is noise that has entered our recorded signal. If we assume that a noisy signal or image is noiseless when we try to make a statistical estimate of g, our estimate will be incorrect. In turn, our estimate of ƒ will also be incorrect. The lower the signaltonoise ratio, the worse our estimate of the deconvolved signal will be. That is the reason why usually inverse filtering the signal is not a good solution. However, if we have at least some knowledge of the type of noise in the data (for example, white noise), we may be able to improve the estimate of ƒ through techniques such as Wiener deconvolution.
The foundations for deconvolution and timeseries analysis were largely laid by Norbert Wiener of the Massachusetts Institute of Technology in his book Extrapolation, Interpolation, and Smoothing of Stationary Time Series (1949).^{[2]} The book was based on work Wiener had done during World War II but that had been classified at the time. Some of the early attempts to apply these theories were in the fields of weather forecasting and economics.
Contents
Applications of deconvolution
Seismology
The concept of deconvolution had an early application in reflection seismology. In 1950, Enders Robinson was a graduate student at MIT. He worked with others at MIT, such as Norbert Wiener, Norman Levinson, and economist Paul Samuelson, to develop the "convolutional model" of a reflection seismogram. This model assumes that the recorded seismogram s(t) is the convolution of an Earthreflectivity function e(t) and a seismic wavelet w(t) from a point source, where t represents recording time. Thus, our convolution equation is
The seismologist is interested in e, which contains information about the Earth's structure. By the convolution theorem, this equation may be Fourier transformed to
in the frequency domain. By assuming that the reflectivity is white, we can assume that the power spectrum of the reflectivity is constant, and that the power spectrum of the seismogram is the spectrum of the wavelet multiplied by that constant. Thus,
If we assume that the wavelet is minimum phase, we can recover it by calculating the minimum phase equivalent of the power spectrum we just found. The reflectivity may be recovered by designing and applying a Wiener filter that shapes the estimated wavelet to a Dirac delta function (i.e., a spike). The result may be seen as a series of scaled, shifted delta functions (although this is not mathematically rigorous):
 ,
where N is the number of reflection events, τ_{ i} τ_{ i} are the reflection times of each event, and r_{ i} are the reflection coefficients.
In practice, since we are dealing with noisy, finite bandwidth, finite length, discretely sampled datasets, the above procedure only yields an approximation of the filter required to deconvolve the data. However, by formulating the problem as the solution of a Toeplitz matrix and using Levinson recursion, we can relatively quickly estimate a filter with the smallest mean squared error possible. We can also do deconvolution directly in the frequency domain and get similar results. The technique is closely related to linear prediction.
Optics and other imaging
In optics and imaging, the term "deconvolution" is specifically used to refer to the process of reversing the optical distortion that takes place in an optical microscope, electron microscope, telescope, or other imaging instrument, thus creating clearer images. It is usually done in the digital domain by a software algorithm, as part of a suite of microscope image processing techniques. Deconvolution is also practical to sharpen images that suffer from fast motion or jiggles during capturing. Early Hubble Space Telescope images were distorted by a flawed mirror and could be sharpened by deconvolution.
The usual method is to assume that the optical path through the instrument is optically perfect, convolved with a point spread function (PSF), that is, a mathematical function that describes the distortion in terms of the pathway a theoretical point source of light (or other waves) takes through the instrument.^{[3]} Usually, such a point source contributes a small area of fuzziness to the final image. If this function can be determined, it is then a matter of computing its inverse or complementary function, and convolving the acquired image with that. The result is the original, undistorted image.
In practice, finding the true PSF is impossible, and usually an approximation of it is used, theoretically calculated^{[4]} or based on some experimental estimation by using known probes. Real optics may also have different PSFs at different focal and spatial locations, and the PSF may be nonlinear. The accuracy of the approximation of the PSF will dictate the final result. Different algorithms can be employed to give better results, at the price of being more computationally intensive. Since the original convolution discards data, some algorithms use additional data acquired at nearby focal points to make up some of the lost information. Regularization in iterative algorithms (as in expectationmaximization algorithms) can be applied to avoid unrealistic solutions.
When the PSF is unknown, it may be possible to deduce it by systematically trying different possible PSFs and assessing whether the image has improved. This procedure is called blind deconvolution.^{[3]} Blind deconvolution is a wellestablished image restoration technique in astronomy, where the point nature of the objects photographed exposes the PSF thus making it more feasible. It is also used in fluorescence microscopy for image restoration, and in fluorescence spectral imaging for spectral separation of multiple unknown fluorophores. The most common iterative algorithm for the purpose is the Richardson–Lucy deconvolution algorithm; the Wiener deconvolution (and approximations) are the most common noniterative algorithms.
Radio astronomy
When performing image synthesis in radio interferometry, a specific kind of radio astronomy, one step consists of deconvolving the produced image with the "dirty beam", which is a different name for the point spread function. A common used method is the CLEAN algorithm.
See also
 Digital filter
 Filter (signal processing)
 Filter design
 Minimum phase
 Independent component analysis
 Wiener deconvolution
 Richardson–Lucy deconvolution
 Digital room correction
 Free deconvolution
 Point spread function
References
 ^ O'Haver T. "Intro to Signal Processing  Deconvolution". University of Maryland at College Park. http://www.wam.umd.edu/~toh/spectrum/Deconvolution.html. Retrieved 20070815.
 ^ Wiener N (1964). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Cambridge, Mass: MIT Press. ISBN 0262730057.
 ^ ^{a} ^{b} Cheng PC (2006). "The Contrast Formation in Optical Microscopy". Handbook of Biological Confocal Microscopy (Pawley JB, ed.) (3rd ed. ed.). Berlin: Springer. pp. 189–90. ISBN 038725921x.
 ^ Nasse M. J., Woehl J. C. (2010). "Realistic modeling of the illumination point spread function in confocal scanning optical microscopy". J. Opt. Soc. Am. A 27 (2): 295–302. doi:10.1364/JOSAA.27.000295.
External links
 Presentations
 Use of a deconvolution process to cancel complex motion/shake in photography(in French) next and results by JM Pichot Many photos, and no math!(in French)
 Flash presentation of blind deconvolution and source separation problem
 3D simulations of deconvolution applied to Digital Room Correction
 Deconvolution Explanation and Examples
 Tutorials and techniques
 Deconvolution in optical microscopy
 A summary of blind deconvolution techniques.
 Biophotonics article on deconvolution (PDF)
 Deconvolution Tutorial
 Software
 Mathematica ImageDeconvolve function
 Mathematica ListDeconvolve function
 Focus Magic, Software that uses deconvolution to fix outoffocus blur and motion blur in an image
 Unshake, a blind deconvolver Java program for terrestrial photographs (currently freeware)
 SVIwiki on 3D microscopy and deconvolution
 Tria Image Processing, Applies deconvolution and blind deconvolution to quickly remove blur from images
 DeblurMyImage from ADPTools  uses stateofart deconvolution methods for outoffocus correction and motion correction
 bitplane
 Other
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