i m working on Bayes shrink will you please explain the code

[thr,sorh,keepapp]= ddencmp('den', 'wv', noisyImage);

%% Wavelet Decomposition.

[C, S] = wavedec2(noisyImage, decomposingLevels, waveletFilterType);

display(S); %% Bayesian Thresholding.

ST = S(1, 1)^2 + 1; display(ST); bayesCoeff = [C(1 : ST-1), zeros(1, length(ST : 1 : length(C)))]; noiseVar = length(C)-S(size(S,1)-1,1)^2+1;

noiseSigma = median(abs(C(noiseVar : length(C)))) / 0.6745;

for index = 2 : size(S, 1) - 1

    % H Coefficients.
    coefH = C(ST : ST + S(index,1)^2 - 1);
   thr = ProposedThreshold(coefH, noiseSigma);
    bayesCoeff(ST : ST + S(index, 1)^2 - 1) = SoftThreshold(coefH,thr);
    ST = ST + S(index, 1)^2;

    % V Coefficients.
    coefV = C(ST : ST + S(index, 1)^2 - 1);
   thr = ProposedThreshold(coefV, noiseSigma);
    bayesCoeff(ST : ST + S(index, 1)^2 - 1) = SoftThreshold(coefV,thr);
    ST=ST+S(index,1)^2;

    % D Coefficients.
    coefD = C(ST : ST + S(index, 1)^2 - 1);
   thr = ProposedThreshold(coefD, noiseSigma);
    bayesCoeff(ST : ST + S(index, 1)^2 - 1) = SoftThreshold(coefD,thr);
    ST = ST + S(index, 1)^2;
end

bayesImage = waverec2(bayesCoeff, S, waveletFilterType);

Answer 1

[Hari]

Hi Ashima,

This is the MATLAB code for denoising an image using wavelet decomposition and Bayesian thresholding.

I assume that the functions "ProposedThreshold" and "SoftThreshold" are defined elsewhere in your code or workspace, as they are not standard MATLAB functions.

Here is the explanation for the important steps:

Initial Setup and Wavelet Decomposition:

The code initializes parameters for denoising using ddencmp and performs a 2D wavelet decomposition on the noisy image using wavedec2.

[thr, sorh, keepapp] = ddencmp('den', 'wv', noisyImage);
[C, S] = wavedec2(noisyImage, decomposingLevels, waveletFilterType);

Calculate Noise Statistics:

The noise variance and sigma are estimated using the wavelet coefficients. The median function is used to estimate the noise level.

noiseVar = length(C) - S(size(S,1)-1,1)^2 + 1;
noiseSigma = median(abs(C(noiseVar : end))) / 0.6745;

Bayesian Thresholding:

The code iterates through the wavelet decomposition levels to apply Bayesian thresholding. For each level, horizontal (H), vertical (V), and diagonal (D) coefficients are thresholded.

for index = 2 : size(S, 1) - 1
    % Process H, V, and D coefficients
    coefH = C(ST : ST + S(index,1)^2 - 1);
    thr = ProposedThreshold(coefH, noiseSigma);
    bayesCoeff(ST : ST + S(index, 1)^2 - 1) = SoftThreshold(coefH, thr);
    % Repeat for V and D coefficients
end

Reconstruction of Denoised Image:

The denoised image is reconstructed from the thresholded coefficients using waverec2.

bayesImage = waverec2(bayesCoeff, S, waveletFilterType);

Refer to the documentation of "ddencmp": https://www.mathworks.com/help/wavelet/ref/ddencmp.html

Refer to the documentation of "wavedec2": https://www.mathworks.com/help/wavelet/ref/wavedec2.html

Refer to the documentation of "waverec2": https://www.mathworks.com/help/wavelet/ref/waverec2.html

Hope this helps!