function [deblurred_image] = OneShotMaxPol(image_scan_original, deblurring_kernel, model_type, alpha_estimate, c1_estimate, h_psf, significany)

[N_1, N_2, N_3] = size(image_scan_original);

l = (numel(h_psf(:,1))-1)/2;

x_polynomial = [-l:l];

fitting_function = @(amp, alpha, beta, x) generalized_Gaussian_for_fitting(x, amp, alpha, beta);

for channel = 1: N_3

    switch model_type

        case 'Gaussian'

            beta = 1.8;

            sigm = alpha_estimate(channel)*0.85;

        case 'Laplacian'

            beta = 0.9;

            sigm = alpha_estimate(channel)*0.5;

    end

    %  generate GG smooting filter

    h_GGaussian = fitting_function(1, sigm, beta, x_polynomial);

    h_GGaussian = h_GGaussian/sum(h_GGaussian);

    % generate inverse deconvolution filter

    inverse_deblurring_kernel = conv(deblurring_kernel(:, channel), h_GGaussian(:), 'same');

    %  apply inverse deblurring kernel on image observation

    deblurring_edges_1 = imfilter(image_scan_original(:,:,channel), inverse_deblurring_kernel, 'symmetric', 'conv');

    deblurring_edges_2 = imfilter(image_scan_original(:,:,channel), inverse_deblurring_kernel', 'symmetric', 'conv');

    deblurring_edges_12 = imfilter(deblurring_edges_1, inverse_deblurring_kernel', 'symmetric', 'conv');

    deblurring_edges = deblurring_edges_1 + deblurring_edges_2 + deblurring_edges_12;

    %

    gamma_significance(channel) = entropy(image_scan_original(:,:,channel))/(entropy(deblurring_edges)+0.5)*significany;

    deblurred_image(:,:,channel) = image_scan_original(:,:,channel) + gamma_significance(channel) * deblurring_edges;

end

%  convert double format into uint8 format

deblurred_image = im2uint8(deblurred_image);