// Frequency-space gaussian deconvolution is pretty simple (outside of the fourier transforms)
// Output = fft(filter) * fft(image) / (fft(filter)^2 + weight*fft(gradient filters)^2)
Image FFTDeconvolve::apply(Window im, Window filter, float weight) {
assert(im.frames == 1, "-fftdeconvolve can only handle single-frame images\n");
assert(filter.frames == 1, "-fftdeconvolve can only handle single-frame filters\n");
assert(filter.channels == 1, "-fftdeconvolve can only handle single-channel filters\n");
// sum of second derivatives filter
Image fft_g(im.width, im.height, 1, 2);
fft_g(0, 0)[0] = weight;
fft_g(im.width-1, 0)[0] = -weight*0.25;
fft_g(0, im.height-1,0)[0] = -weight*0.25;
fft_g(1, 0)[0] = -weight*0.25;
fft_g(0, 1)[0] = -weight*0.25;
FFT::apply(fft_g);
Image fft_im = RealComplex::apply(im);
FFT::apply(fft_im);
Image fft_filter(im.width, im.height, 1, 2);
for (int y = 0; y < filter.height; y++) {
int fy = y - filter.height/2;
if (fy < 0) fy += fft_filter.height;
for (int x = 0; x < filter.width; x++) {
for (int c = 0; c < filter.channels; c++) {
int fx = x - filter.width/2;
if (fx < 0) fx += fft_filter.width;
fft_filter(fx, fy)[2*c] = filter(x, y)[c];
}
}
}
FFT::apply(fft_filter);
ComplexMultiply::apply(fft_im, fft_filter, true);
ComplexMultiply::apply(fft_filter, fft_filter, true);
Add::apply(fft_filter, fft_g);
ComplexDivide::apply(fft_im, fft_filter, false);
IFFT::apply(fft_im);
return ComplexReal::apply(fft_im);
}
static void
process (Mat const &src, Mat &dst)
{
fft_filter (src, frequency_filter, NULL, &dst);
}
