Image Noise Filter Based on DCT and Fast Clustering
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An algorithm for filtering images contaminated by additive white Gaussian noise in discrete cosine transform domain is proposed. The algorithm uses a clustering stage to obtain mean power spectrum of each cluster. The groups of clusters are found by the proposed fast algorithm based on 2D histograms and watershed transform. In addition to the mean spectrum of each cluster, the local groups of similar patches are found to obtain the local spectrum, and therefore, derive the local Wiener filter frequency response better and perform the collaborative filtering over the groups of patches. The obtained filtering results are compared to the state-of-the-art filters in terms of peak signal-to-noise ratio and structural similarity index. It is shown that the proposed algorithm is competitive in terms of signal-to-noise ratio and in almost all cases is superior to the state-of-the art filters in terms of structural similarity.
KeywordsNoise suppression Collaborative filtering Fast image clustering
This work partially was supported by Instituto Politecnico Nacional as a part of research project SIP# 20171559 .
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