Error during implementation of Wavelet based denoising using hybrid approach of adaptive flitering and single value decomposition
I want to denoise the signal using hybrid approach of adaptive flitering and single value decomposition. I have written the following code but it’s showing error in Line 32
S_thresh = diag(S – threshold*(S > threshold));
Following is the code and signal image with nosie:
clear all
clc
load("Signal")
% Load the noisy signal or image
noisy_signal = C;
% Set parameters
wavelet = ‘db2’; % Choose wavelet type
level = 5; % Decomposition level
threshold_type = ‘s’; % ‘s’ for soft thresholding, ‘h’ for hard thresholding
threshold_multiplier = median(abs(C)) / 0.6745; % Threshold multiplier for adaptive thresholding
% Perform wavelet decomposition
[c, l] = wavedec2(noisy_signal, level, wavelet);
% Initialize denoised coefficients
denoised_c = cell(1, level + 1);
% Loop through each detail coefficient subband for denoising
for i = 1:level
% Extract detail coefficients for current subband
cd = detcoef2(‘all’, c, l, i);
% Apply adaptive filtering
denoised_cd = zeros(size(cd));
for j = 1:size(cd, 1)
% Apply adaptive thresholding using singular value decomposition
[U, S, V] = svd(cd(j, :));
if threshold_type == ‘s’
threshold = threshold_multiplier * median(diag(S));
S_thresh = diag(S – threshold*(S > threshold));
elseif threshold_type == ‘h’
S_thresh = diag(S.*(abs(S) > threshold_multiplier * median(diag(S))));
end
denoised_cd(j, 🙂 = U * S_thresh * V’;
end
% Update denoised coefficients
denoised_c{i} = denoised_cd;
end
% Keep approximation coefficients as is
denoised_c{level + 1} = appcoef2(c, l, wavelet, level);
% Reconstruct the denoised signal or image
denoised_signal = waverec2(denoised_c, l, wavelet);
% Display the results
figure;
subplot(1, 2, 1);
imshow(noisy_signal);
title(‘Noisy Signal’);
subplot(1, 2, 2);
imshow(uint8(denoised_signal));
title(‘Denoised Signal’);I want to denoise the signal using hybrid approach of adaptive flitering and single value decomposition. I have written the following code but it’s showing error in Line 32
S_thresh = diag(S – threshold*(S > threshold));
Following is the code and signal image with nosie:
clear all
clc
load("Signal")
% Load the noisy signal or image
noisy_signal = C;
% Set parameters
wavelet = ‘db2’; % Choose wavelet type
level = 5; % Decomposition level
threshold_type = ‘s’; % ‘s’ for soft thresholding, ‘h’ for hard thresholding
threshold_multiplier = median(abs(C)) / 0.6745; % Threshold multiplier for adaptive thresholding
% Perform wavelet decomposition
[c, l] = wavedec2(noisy_signal, level, wavelet);
% Initialize denoised coefficients
denoised_c = cell(1, level + 1);
% Loop through each detail coefficient subband for denoising
for i = 1:level
% Extract detail coefficients for current subband
cd = detcoef2(‘all’, c, l, i);
% Apply adaptive filtering
denoised_cd = zeros(size(cd));
for j = 1:size(cd, 1)
% Apply adaptive thresholding using singular value decomposition
[U, S, V] = svd(cd(j, :));
if threshold_type == ‘s’
threshold = threshold_multiplier * median(diag(S));
S_thresh = diag(S – threshold*(S > threshold));
elseif threshold_type == ‘h’
S_thresh = diag(S.*(abs(S) > threshold_multiplier * median(diag(S))));
end
denoised_cd(j, 🙂 = U * S_thresh * V’;
end
% Update denoised coefficients
denoised_c{i} = denoised_cd;
end
% Keep approximation coefficients as is
denoised_c{level + 1} = appcoef2(c, l, wavelet, level);
% Reconstruct the denoised signal or image
denoised_signal = waverec2(denoised_c, l, wavelet);
% Display the results
figure;
subplot(1, 2, 1);
imshow(noisy_signal);
title(‘Noisy Signal’);
subplot(1, 2, 2);
imshow(uint8(denoised_signal));
title(‘Denoised Signal’);Â I want to denoise the signal using hybrid approach of adaptive flitering and single value decomposition. I have written the following code but it’s showing error in Line 32
S_thresh = diag(S – threshold*(S > threshold));
Following is the code and signal image with nosie:
clear all
clc
load("Signal")
% Load the noisy signal or image
noisy_signal = C;
% Set parameters
wavelet = ‘db2’; % Choose wavelet type
level = 5; % Decomposition level
threshold_type = ‘s’; % ‘s’ for soft thresholding, ‘h’ for hard thresholding
threshold_multiplier = median(abs(C)) / 0.6745; % Threshold multiplier for adaptive thresholding
% Perform wavelet decomposition
[c, l] = wavedec2(noisy_signal, level, wavelet);
% Initialize denoised coefficients
denoised_c = cell(1, level + 1);
% Loop through each detail coefficient subband for denoising
for i = 1:level
% Extract detail coefficients for current subband
cd = detcoef2(‘all’, c, l, i);
% Apply adaptive filtering
denoised_cd = zeros(size(cd));
for j = 1:size(cd, 1)
% Apply adaptive thresholding using singular value decomposition
[U, S, V] = svd(cd(j, :));
if threshold_type == ‘s’
threshold = threshold_multiplier * median(diag(S));
S_thresh = diag(S – threshold*(S > threshold));
elseif threshold_type == ‘h’
S_thresh = diag(S.*(abs(S) > threshold_multiplier * median(diag(S))));
end
denoised_cd(j, 🙂 = U * S_thresh * V’;
end
% Update denoised coefficients
denoised_c{i} = denoised_cd;
end
% Keep approximation coefficients as is
denoised_c{level + 1} = appcoef2(c, l, wavelet, level);
% Reconstruct the denoised signal or image
denoised_signal = waverec2(denoised_c, l, wavelet);
% Display the results
figure;
subplot(1, 2, 1);
imshow(noisy_signal);
title(‘Noisy Signal’);
subplot(1, 2, 2);
imshow(uint8(denoised_signal));
title(‘Denoised Signal’); wavelet denoising, partial discharge signal, single value decomopsition (svd), adaptive flitering MATLAB Answers — New Questions
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