Image mixed gaussian and impulse noise elimination based on sparse representation model

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The weights the imposed images are incorporated into a variational framework to treat more complex noise distribution. Such characterized by heavy tails caused which needs be eliminated through proper weighting of residual. outliers has significant effect weights. Hence accurate residual error initialization plays important role overall performance, especially at high rates. In paper, free image, an easier implement parameter-free procedure for updating have been proposed. Experimental results demonstrate capability strategy recover highly corrupted as compared state art algorithm. achieved motivate us practice.</p>