This paper presents a new image denoising method based on sparse reconstruction by dictionary learning and collaborative filtering. First, we form an affinity net, in which a node represents an image patch, for the given image by clustering similar patches. For each cluster, we learn an undercomplete dictionary and represent clusters nodes by imposing sparsity inducing norm as a combination of few atoms. Depending on its affinity to other nodes, a single node could be present in multiple clusters making the clusters overlapping. This enables a single global estimation for each filtered pixel to be obtained by collaboratively aggregating its reconstructed patches in the corresponding clusters. Extensive experimental results demonstrate superior performance for additive noise removal without requiring the correct noise variance.
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