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WaveletShrinkageTest.m
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WaveletShrinkageTest.m
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% CSCI 631: Foundations of Computer Vision
%
% Project Title: Image Denoising using Wavelet Shrinkage
%
% Created by: Dhaval Chauhan (dmc8686@rit.edu)
% Created by: Anuja Vane (asv1612@rit.edu)
% Computer Science Department
% Rochester Institute of Technology
%
% Created on: 21, November, 2017
%
% A program to see comparisons between multiple noise reduction techniques.
% User will pass path of a clean image and this program will add noise to
% it and then denoise it for the user to see the difference between
% performance of multiple methods.
% This function takes in path of an image file, checks whether its a color
% image or a grayscale image, and calls a function that does wavelet
% shrinkage function on the input image.
function WaveletShrinkageTest( input_image_path )
% Read in the input image.
im = imread( input_image_path );
% Get the dimensions of the input image.
dims = size( im );
% Check if it a color or a grayscale image and call the denoise
% function.
if ( length(dims) > 2 )
% Call denoise method for a color image.
wavelet_denoise( im, 'c' );
else
% Call denoise method for a grayscale image.
wavelet_denoise( im, 'g' );
end
end
% This function performs denoising on the input image and shows the output
% results of all the denoising method.
function wavelet_denoise( im , color_or_gray )
% If the image is in grayscale.
if color_or_gray == 'g'
% Introduce Gaussian noise with mean 0 and standard deviation 0.01
im_noise = imnoise( im, 'gaussian', 0, 0.01 );
% Do median filtering on the noisy image.
im_median_fltrd = medfilt2( im_noise );
% Create a Gaussian filter of size 3 and standard deviation 5.
fltr = fspecial( 'gaussian', 3, 5 );
% Filter the noisy image using the gaussian filter matrix obtained
% above.
im_lclavg_fltrd = imfilter( im_noise, fltr, 'same', 'repl' );
% Define a wavelet variable that uses a Bi-orthogonal wavelet with
% 5 vanishing levels and 3 reconstruction levels.
wavelet_name = 'bior3.5';
% Number of decomposition levels
decomposition_level = 5;
% Do wavelet decomposition after converting the noisy image to
% Bi-orthogonal wavelet domain and get the decomposition
% vector.
[decomp_vector, S] = wavedec2( im_noise, decomposition_level, wavelet_name );
% Get horizontal, vertical and diagonal coefficients using the
% wavelet and the decomposition level 5.
[H1,V1,D1] = detcoef2( 'all', decomp_vector, S, 5 );
% Get approximate coefficients from wavelet and the decomposition
% of level 5.
A1 = appcoef2(decomp_vector,S,'bior3.5',5);
% Get image of the coefficients from level 5 decomposed image.
V1img = wcodemat(V1,255,'mat',5);
H1img = wcodemat(H1,255,'mat',5);
D1img = wcodemat(D1,255,'mat',5);
A1img = wcodemat(A1,255,'mat',5);
% Display the coefficient images.
figure;
imagesc(V1img);
figure;
imagesc(H1img);
figure;
imagesc(D1img);
figure;
imagesc(A1img);
figure;
% Get inverse 2D DWT of level 5 decomposed image and display it.
out = idwt2( A1,H1,V1,D1,'bior3.5' );
figure;
imagesc(out);
% Get a 2D level dependent threshold using the penalhi method.
threshold1 = wthrmngr( 'dw2ddenoLVL', 'penalhi', decomp_vector, S, 5 );
% set threshold type as Soft Threshold
sorh = 's';
% Get a denoised image using the above threshold of type Soft.
[im_ws_s, CXC, LXC] = wdencmp( 'lvd', decomp_vector, S, ...
wavelet_name, decomposition_level, threshold1, sorh );
% set threshold type as Hard Threshold
sorh = 'h';
% Get a denoised image using the above threshold of type Hard.
[im_ws_h, CXC, LXC] = wdencmp( 'lvd', decomp_vector, S, ...
wavelet_name, decomposition_level, threshold1, sorh );
% Display original input image.
figure;
imshow(im); colormap gray; axis off;
title('Original Image');
% Display noisy image.
figure;
imshow(im_noise); colormap gray; axis off;
title('Noise introduced Image');
% Display local averaged image.
figure;
imshow(im_lclavg_fltrd); colormap gray; axis off;
title('Gaussian Blur filtering');
% Display median filtered image.
figure;
imshow(im_median_fltrd); colormap gray; axis off;
title('Median filtering');
% Display wavelet denoised using hard threshold image
figure;
% Convert to uint8 format.
im_ws_h_norm = im_ws_h - min(im_ws_h(:));
im_ws_h_norm = im_ws_h_norm ./ max(im_ws_h_norm(:));
im_ws_h = im2uint8( im_ws_h_norm );
imshow(im_ws_h); colormap gray; axis off;
title('Wavelet Shrinkage using a hard threshold');
% Display wavelet denoised using soft threshold image
figure;
% Convert to uint8 format.
im_ws_s_norm = im_ws_s - min(im_ws_s(:));
im_ws_s_norm = im_ws_s_norm ./ max(im_ws_s_norm(:));
im_ws_s = im2uint8( im_ws_s_norm );
imshow(im_ws_s); colormap gray; axis off;
title('Wavelet Shrinkage using a soft threshold');
% Show surface of noisy image.
figure;
mesh(im_noise);
% Show surface of soft thresholded image.
figure;
mesh(im_ws_s);
% Calculate mean square errors of the output images.
im_mse_med = immse( im, im_median_fltrd );
im_mse_lclavg = immse( im, im_lclavg_fltrd );
im_mse_ws_h = immse( im, im_ws_h );
im_mse_ws_s = immse( im, im_ws_s );
% Display the MSEs in console.
disp( im_mse_med );format long g;
disp( im_mse_lclavg );format long g;
disp( im_mse_ws_h );format long g;
disp( im_mse_ws_s );format long g;
% If the input image is a Color image.
else
% Get the Red, Green, and Blue channels.
im_r = im(:,:,1);
im_g = im(:,:,2);
im_b = im(:,:,3);
% Introduce noise in all 3 channels.
im_noise_r = imnoise( im_r, 'gaussian', 0, 0.03 );
im_noise_g = imnoise( im_g, 'gaussian', 0, 0.03 );
im_noise_b = imnoise( im_b, 'gaussian', 0, 0.03 );
% Merge all 3 channels to 1 3D image matrix again.
im_noise = im;
im_noise(:,:,1) = im_noise_r;
im_noise(:,:,2) = im_noise_g;
im_noise(:,:,3) = im_noise_b;
% Do median filtering on the noisy image.
im_median_fltrd = im;
im_median_fltrd(:,:,1) = medfilt2( im_noise_r );
im_median_fltrd(:,:,2) = medfilt2( im_noise_g );
im_median_fltrd(:,:,3) = medfilt2( im_noise_b );
% Do local averaging on the noisy image.
fltr = fspecial( 'gaussian', 3, 5 );
im_lclavg_fltrd = im;
im_lclavg_fltrd(:,:,1) = imfilter( im_noise_r, fltr, 'same', 'repl' );
im_lclavg_fltrd(:,:,2) = imfilter( im_noise_g, fltr, 'same', 'repl' );
im_lclavg_fltrd(:,:,3) = imfilter( im_noise_b, fltr, 'same', 'repl' );
% Define a wavelet variable that uses a Bi-orthogonal wavelet with
% 5 vanishing levels and 3 reconstruction levels.
wavelet_name = 'bior3.5';
% Number of decomposition levels
decomposition_level = 5;
% Do wavelet decomposition after converting the noisy image to
% Bi-orthogonal wavelet domain and get the decomposition
% vector.
[decomp_vector_r, S_r] = wavedec2( im_noise_r, decomposition_level, wavelet_name );
[decomp_vector_g, S_g] = wavedec2( im_noise_g, decomposition_level, wavelet_name );
[decomp_vector_b, S_b] = wavedec2( im_noise_b, decomposition_level, wavelet_name );
% Get a 2D level dependent threshold using the penalhi method.
threshold_r = wthrmngr( 'dw2ddenoLVL', 'penalhi', decomp_vector_r, S_r, 3);
threshold_g = wthrmngr( 'dw2ddenoLVL', 'penalhi', decomp_vector_g, S_g, 3);
threshold_b = wthrmngr( 'dw2ddenoLVL', 'penalhi', decomp_vector_b, S_b, 3);
% set threshold type as Soft Threshold
sorh = 's';
% Get a denoised image using the above threshold of type Soft.
[im_ws_r_s, CXC, LXC] = wdencmp('lvd', decomp_vector_r, S_r, ...
wavelet_name, decomposition_level, threshold_r,sorh);
[im_ws_g_s, CXC, LXC] = wdencmp('lvd', decomp_vector_g, S_g, ...
wavelet_name, decomposition_level, threshold_g,sorh);
[im_ws_b_s, CXC, LXC] = wdencmp('lvd', decomp_vector_b, S_b, ...
wavelet_name, decomposition_level, threshold_b,sorh);
% set threshold type as Hard Threshold
sorh = 'h';
% Get a denoised image using the above threshold of type Hard.
[im_ws_r_h, CXC, LXC] = wdencmp('lvd', decomp_vector_r, S_r, ...
wavelet_name, decomposition_level, threshold_r,sorh);
[im_ws_g_h, CXC, LXC] = wdencmp('lvd', decomp_vector_g, S_g, ...
wavelet_name, decomposition_level, threshold_g,sorh);
[im_ws_b_h, CXC, LXC] = wdencmp('lvd', decomp_vector_b, S_b, ...
wavelet_name, decomposition_level, threshold_b,sorh);
% Merge all the soft thresholded channels to 1 3D image.
im_ws_s = im;
im_ws_s(:,:,1) = im_ws_r_s;
im_ws_s(:,:,2) = im_ws_g_s;
im_ws_s(:,:,3) = im_ws_b_s;
% Merge all the hard thresholded channels to 1 3D image.
im_ws_h = im;
im_ws_h(:,:,1) = im_ws_r_h;
im_ws_h(:,:,2) = im_ws_g_h;
im_ws_h(:,:,3) = im_ws_b_h;
% Display original input image
figure;
imshow(im); colormap gray; axis off;
title('Original Image');
% Display noisy image.
figure;
imshow(im_noise); colormap gray; axis off;
title('Noise introduced Image');
% Display Local averaged image.
figure;
imshow(im_lclavg_fltrd); colormap gray; axis off;
title('Gaussian Blur filtering');
% Display Median filtered image.
figure;
imshow(im_median_fltrd); colormap gray; axis off;
title('Median filtering');
% Display image with hard threshold denoising.
figure;
imshow(im_ws_h); colormap gray; axis off;
title('Wavelet Shrinkage using a hard threshold');
% Display image with soft threshold denoising.
figure;
imshow(im_ws_s); colormap gray; axis off;
title('Wavelet Shrinkage using a soft threshold');
end
end