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  • understanding the kernel estimation algorithm

    From khouloud.guemri.tn@ieee.org@21:1/5 to All on Thu Apr 28 19:58:17 2016
    hello,

    can any one explain me step by setp the algorithm bellow:
    function psf = estimate_psf(blurred_x, blurred_y, latent_x, latent_y, weight, psf_size)

    %----------------------------------------------------------------------
    % these values can be pre-computed at the beginning of each level
    % blurred_f = fft2(blurred);
    % dx_f = psf2otf([1 -1 0], size(blurred));
    % dy_f = psf2otf([1;-1;0], size(blurred));
    % blurred_xf = dx_f .* blurred_f; %% FFT (Bx)
    % blurred_yf = dy_f .* blurred_f; %% FFT (By)

    latent_xf = fft2(latent_x);
    latent_yf = fft2(latent_y);
    blurred_xf = fft2(blurred_x);
    blurred_yf = fft2(blurred_y);
    % compute b = sum_i w_i latent_i * blurred_i
    b_f = conj(latent_xf) .* blurred_xf ...
    + conj(latent_yf) .* blurred_yf;
    b = real(otf2psf(b_f, psf_size));

    p.m = conj(latent_xf) .* latent_xf ...
    + conj(latent_yf) .* latent_yf;
    %p.img_size = size(blurred);
    p.img_size = size(blurred_xf);
    p.psf_size = psf_size;
    p.lambda = weight;

    psf = ones(psf_size) / prod(psf_size);
    psf = conjgrad(psf, b, 20, 1e-5, @compute_Ax, p);

    psf(psf < max(psf(:))*0.05) = 0;
    psf = psf / sum(psf(:));
    end

    function y = compute_Ax(x, p)
    x_f = psf2otf(x, p.img_size);
    y = otf2psf(p.m .* x_f, p.psf_size);
    y = y + p.lambda * x;
    end


    THANKS

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