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scott wrote:
>> Your 1D example seemed to be about the image boundaries rather than
>> the image being represented by real/complex numbers. The point about
>> the image boundaries is true, and will prevent an exact deconvolution
>> as you pointed out. You can still often get a good approximate
>> solution by either assuming boundary conditions or by using an image
>> prior. I believe the latter is these is what's generally used in
>> practice for image deblurring.
>
> The example I was referring to was this one:
>
> Convolution kernel: {1,2,1}
> Source image: {a,b,c,d,e,f,g,h}
> Result: {12,12,12,12,12,12,12,12}
>
> What is the source image?
>
I think my last post was not clear enough. I'm agreeing with you that
you can't exactly recover the source image in your example. What I'm
disagreeing with are two things:
1) This has anything to do with complex numbers, even if you use the FFT
method to recover the source image.
2) This prevents you from getting an approximate answer in practice.
Both of these assertions can be tested pretty easily, so I'll treat them
in turn. Also, sorry for the long post, but it seemed useful to go into
a fair bit of detail.
---------- (1) complex numbers aren't the issue
First of all, let's try the FFT method for solving for the source image.
This code should run in both matlab and octave:
n = 8; % size of images
k = [1 2 1]; % kernel
k2 = [zeros(1,ceil((n-3)/2)) k zeros(1,floor((n-3)/2))]; % padded kernel
y = 12 + zeros(1,n); % result image
disp('source image (FFT method)');
ifft(fft(y)./(fft(k2)+1e-8)) % +1e-8 is a regularization hack
>>>>>> result:
{3 3 3 3 3 3 3 3}
So despite not having any complex numbers in "the photo" (i.e. the
result image) the method worked just fine and gave us a pretty
reasonable looking result.
"But there's not enough information to derive a result!" I imagine
you're thinking. This is correct, so clearly something was happening
behind the scenes that allows the method to actually compute a source
image. The answer is that the FFT method makes an implicit assumption
about what happens beyond the boundaries of the result image by treating
the image as cyclic. Thus the source image isn't just
{a,b,c,d,e,f,g,h}, it's actually treated as
{...,f,g,h,a,b,c,d,e,f,g,h,a,b,c,...}, which is what allows this method
to work.
This is the point I was making earlier that one way to solve the problem
is to make an assumption about what happens beyond the boundaries of the
image. A natural next question to ask is what sort of errors this
outright assumption might give in the reconstructed source image.
---------- (2) approximate solutions are possible
Let's look at the source images reconstructed under three different
"reasonable" assumptions about what happens beyond the boundaries of the
image:
cyclic: {...,f,g,h,a,b,c,d,e,f,g,h,a,b,c,...}
zero: {...,0,0,0,a,b,c,d,e,f,g,h,0,0,0,...}
full-intensity: {...,12,12,12,a,b,c,d,e,f,g,h,12,12,12,...}
For each case the resulting source images are:
cyclic-source: {3 3 3 3 3 3 3 3}
zero-source: {5.3333, 1.3333, 4.0000, 2.6667, 2.6667, 4.0000, 1.3333,
5.3333}
full-intensity-source: {4.5556, 1.8889, 3.6667, 2.7778, 2.7778, 3.6667,
1.8889, 4.5556}
Clearly the different assumptions have given rise to different
reconstructed images. Nevertheless, notice that the center pixels in
each of the three cases take the values of 3.0, 2.67, and 2.78. Despite
the fact that the assumptions for what happened beyond the boundary
range from 0 to 12, the value of the center pixels only ranges from 2.67
to 3.0 -- a factor of 36 less.
Basically, an incorrect assumption about what happens beyond the edges
of the image resulted in errors concentrated at the edges of the
reconstructed image, so in the center of the source image the FFT method
actually can actually give a pretty accurate answer. For a larger image
(and the same kernel) the center values will be even more accurate.
---------- side notes
I want to emphasize that the deblurring method I gave is only to
illustrate the basic theoretical principles behind using FFT to
accelerate the deconvolution process. In practice inaccurate kernels
and image noise will result in a pretty poor result if you just use the
method I gave. However, people can and have developed pretty good
deblurring methods which are more sophisticated and do work well in
practice. In many (but not all) of these approaches, using the FFT and
performing some operations in frequency space is very useful for
speeding up the algorithm.
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