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We propose a novel two dimensional least mean square (2D-LMS) algorithm for sparse image de-noising, which exploits the sparsity information of images by utilizing a priori. To efficiently accelerate convergence speed, the proposed algorithm utilizes the image sparsity level information to modify the cost function of traditional 2D-LMS via a penalty of tap-weight. Since the largest q norm is not differentiable...
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