Image Denoising Based on the Wavelet Co-Occurrence Matrix
Zeyong Shan, Selin Aviyente · 2006
Image denoising is a well-known problem in signal processing. Wavelet decomposition based approaches have been applied successfully to the image denoising problem. The majority of wavelet thresholding methods do not take the spatial correlation between wavelet coefficients into account. A new image denoising approach is presented; it incorporates the intra-scale dependencies between the wavelet coefficients into the thresholding algorithm. The cooccurrence matrix of the wavelet coefficients and their neighbors is constructed to represent the spatial dependencies. An information-theoretic criterion, the 2D joint entropy of the wavelet cooccurrence matrix, is used as the cost function to determine the optimal threshold. Experimental results indicate that the proposed approach yields significant improvement over universal thresholding, both in visual quality and mean square error.