Jump to Soft or Hard Thresholding - Hard and soft thresholding are examples of shrinkage rules. After you have determined your threshold, you. Download softcam key tanaka t22 jurassic. • • • • • • • • Installing toolboxes and setting up the path. You need to download the and the. You need to unzip these toolboxes in your working directory, so that you have toolbox_general/ and toolbox_signal/ in your directory. For Scilab user: you must replace the Matlab comment '%' by its Scilab counterpart '//'. Recommandation: You should create a text file named for instance numericaltour.sce (in Scilab) or numericaltour.m (in Matlab) to write all the Scilab/Matlab commands you want to execute. Then, simply run exec('numericaltour.sce'); (in Scilab) or numericaltour; (in Matlab) to run the commands. Execute this line only if you are using Matlab. Getd = @(p)path(path,p);% scilab users must *not* execute this Then you can add these toolboxes to the path.% Add some directories to the path getd( 'toolbox_signal/'); getd( 'toolbox_general/'); Image loading and adding Gaussian noise. A simple noise model is additive Gaussian noise. First we load an image. Name = 'boat'; n = 256; M0 = rescale( load_image(name,n) ); Then we add some gaussian noise to it. Sigma =.08;% noise level M = M0 + sigma*randn(size(M0)); Display. Clf; imageplot(M0, 'Original', 1,2,1); imageplot(clamp(M), 'Noisy', 1,2,2); Hard Thresholding vs. Soft Thresholding A thresholding is a 1D non-linear function applied to each wavelet coefficients. The most important thresholding are the hard thresholding (related to L0 minimization) and the soft thresholding (related to L1 minimization). We compute here the 1D thresholding curve.% threshold value T = 1; v = -linspace(-3,3,2000);% hard thresholding of the t values v_hard = v.*(abs(v)>T);% soft thresholding of the t values v_soft = max(1-T./abs(v), 0).*v; Display them. Clf; hold( 'on'); h = plot(v, v_hard); if using_matlab() set(h, 'LineWidth', 2); end h = plot(v, v_soft, 'r--'); if using_matlab() set(h, 'LineWidth', 2); end axis( 'equal'); axis( 'tight'); legend( 'Hard thresholding', 'Soft thresholding'); hold( 'off'); Wavelet Denoising with Hard Thesholding It is possible to perform non linear denoising by thresholding the wavelet coefficients. This allows to better respect the sharp features of the image. Some parameters for the orthogonal wavelet transform. Options.ti = 0; Jmin = 4; First we compute the wavelet coefficients of the noisy image. MW = perform_wavelet_transf(M,Jmin,+1,options); Select the threshold value. In practice a threshold of 3*sigma is close to optimal for natural images. T = 3*sigma; Then we hard threshold the coefficients below the noise level.
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