維納濾波要求未退化影象和噪聲功率譜已知。實際情景沒有這麼多先驗知識。約束最小二乘濾波僅要求噪聲方差和均值的知識。

deconvreg(g,PSF,NOISEPOWER,RANGE)

g是未被汙染影象，NOISEPOWER與||η||2成比例，RANGE為值的範圍。預設範圍[10−9,109],將上述兩個引數排除在引數之外產生逆濾波方案，針對NOISEPOWER比較好的估計是MN[σ2η+ση2]。NOISEPOWER可以得到較好的結果。

close all;
clear all;
clc;
% Display the original image.
I
 
 = imread('1.jpg'); 
[d1,d2,d3] = size(I); 
if(d3 > 1) 
I = rgb2gray(I);
end
I = im2double(I);

[hei,wid,~] = size(I);
subplot(2,3,1),imshow(I);
title('Original Image ');


% Simulate a motion blur.
LEN = 100;
THETA = 11;
PSF = fspecial('motion', LEN, THETA);
blurred = imfilter(I, PSF, 'conv', 'circular'
 
);
subplot(2,3,2), imshow(blurred); title('Blurred Image');


% Simulate additive noise.
noise_mean = 0;
noise_var = 0.00001;
blurred_noisy = imnoise(blurred, 'gaussian', ...
                        noise_mean, noise_var);
subplot(2,3,3), imshow(blurred_noisy)
title('Simulate Blur and Noise')

If = fft2(blurred);
Pf = psf2otf(PSF,[hei,wid]);

% Try restoration using  Home Made Constrained Least Squares Filtering.
p = [0 -1 0;-1 4 -1;0 -1 0];
P = psf2otf(p,[hei,wid]);

gama = 0.001;
If = fft2(blurred_noisy);

numerator = conj(Pf);%計算共軛
denominator = Pf.^2 + gama*(P.^2);

deblurred2 = ifft2( numerator.*If./ denominator );
subplot(2,3,4), imshow(deblurred2)
title('Restoration of Blurred Using Constrained Least Squares Filtering');

subplot(2,3,5); imshow(deconvreg(blurred_noisy, PSF,10e1)); title('Regul in Matlab');