%% Deblurring Images Using a Regularized Filter 
% This example shows how to use regularized deconvolution to deblur images.
% Regularized deconvolution can be used effectively when constraints are
% applied on the recovered image (e.g., smoothness) and limited information
% is known about the additive noise. The blurred and noisy image is restored
% by a constrained least square restoration algorithm that uses a regularized
% filter. 

% Copyright 1993-2012 The MathWorks, Inc.

%% Step 1: Read Image
% The example reads in an RGB image and crops it to be 256-by-256-by-3. The
% deconvreg function can handle arrays of any dimension. 

I = imread('tissue.png');
I = I(125+[1:256],1:256,:);
figure;imshow(I);title('Original Image');
text(size(I,2),size(I,1)+15, ...
    'Image courtesy of Alan Partin, Johns Hopkins University', ...
    'FontSize',7,'HorizontalAlignment','right');

%% Step 2: Simulate a Blur and Noise 
% Simulate a real-life image that could be blurred (e.g., due to camera motion
% or lack of focus) and noisy (e.g., due to random disturbances). The example
% simulates the blur by convolving a Gaussian filter with the true image (using
% imfilter). The Gaussian filter represents a point-spread function, PSF. 

PSF = fspecial('gaussian',11,5);
Blurred = imfilter(I,PSF,'conv');
figure;imshow(Blurred);
title('Blurred');

%%
% We simulate the noise by adding a Gaussian noise of variance V to the blurred
% image (using imnoise). 

V = .02;
BlurredNoisy = imnoise(Blurred,'gaussian',0,V);
figure;imshow(BlurredNoisy);
title('Blurred & Noisy');

%% Step 3: Restore the Blurred and Noisy Image 
% Restore the blurred and noisy image supplying noise power, NP, as the third
% input parameter. To illustrate how sensitive the algorithm is to the value of
% noise power, NP, the example performs three restorations. 
%
% The first restoration, reg1, uses the true NP. Note that the example outputs
% two parameters here. The first return value, reg1, is the restored image. The
% second return value, LAGRA, is a scalar, Lagrange multiplier, on which the
% deconvreg has converged. This value is used later in the example. 

%%
NP = V*prod(size(I)); % noise power
[reg1 LAGRA] = deconvreg(BlurredNoisy,PSF,NP);
figure,imshow(reg1),title('Restored with NP');

%%
% The second restoration, reg2, uses a slightly over-estimated noise power,
% which leads to a poor resolution. 

reg2 = deconvreg(BlurredNoisy,PSF,NP*1.3);
figure;imshow(reg2);
title('Restored with larger NP');

%%
% The third restoration, reg3, is given an under-estimated NP value. This leads
% to an overwhelming noise amplification and "ringing" from the image borders. 

reg3 = deconvreg(BlurredNoisy,PSF,NP/1.3);
figure;imshow(reg3);
title('Restored with smaller NP');

%% Step 4: Reduce Noise Amplification and Ringing 
% Reduce the noise amplification and "ringing" along the boundary of the image 
% by calling the edgetaper function prior to deconvolution. Note how the image
% restoration becomes less sensitive to the noise power parameter. Use the 
% noise power value NP from the previous example. 

Edged = edgetaper(BlurredNoisy,PSF);
reg4 = deconvreg(Edged,PSF,NP/1.3);
figure;imshow(reg4);
title('Edgetaper effect');

%% Step 5: Use the Lagrange Multiplier 
% Restore the blurred and noisy image, assuming that the optimal solution
% is already found and the corresponding Lagrange multiplier, LAGRA, is given.
% In this case, any value passed for noise power, NP, is ignored. 
%
% To illustrate how sensitive the algorithm is to the LAGRA value, the example
% performs three restorations. The first restoration (reg5) uses the LAGRA
% output from the earlier solution (LAGRA output from first solution in
% Step 3).

reg5 = deconvreg(Edged,PSF,[],LAGRA);
figure;imshow(reg5);
title('Restored with LAGRA');

%%
% The second restoration (reg6) uses 100*LAGRA which increases the significance
% of the constraint. By default, this leads to over-smoothing of the image.

reg6 = deconvreg(Edged,PSF,[],LAGRA*100);
figure;imshow(reg6);
title('Restored with large LAGRA');

%%
% The third restoration uses LAGRA/100 which weakens the constraint (the
% smoothness requirement set for the image). It amplifies the noise and
% eventually leads to a pure inverse filtering for LAGRA = 0. 

reg7 = deconvreg(Edged,PSF,[],LAGRA/100);
figure;imshow(reg7);
title('Restored with small LAGRA');

%% Step 6: Use a Different Constraint 
% Restore the blurred and noisy image using a different constraint (REGOP)
% in the search for the optimal solution. Instead of constraining the image
% smoothness (REGOP is Laplacian by default), constrain the image smoothness
% only in one dimension (1-D Laplacian). 

REGOP = [1 -2 1];
reg8 = deconvreg(BlurredNoisy,PSF,[],LAGRA,REGOP);
figure;imshow(reg8);
title('Constrained by 1D Laplacian');


displayEndOfDemoMessage(mfilename)
