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Filtering images of more than one channel is challenging in terms of bothefficiency and effectiveness. By grouping similar patches to utilize theself-similarity and sparse linear approximation of natural images, recentnonlocal and transform-domain methods have been widely used in color andmultispectral image (MSI) denoising. Many related methods focus on the modelingof group level correlation to enhance sparsity, which often resorts to arecursive strategy with a large number of similar patches. The importance ofthe patch level representation is understated. In this paper, we mainlyinvestigate the influence and potential of representation at patch level byconsidering a general formulation with block diagonal matrix. We further showthat by training a proper global patch basis, along with a local principalcomponent analysis transform in the grouping dimension, a simpletransform-threshold-inverse method could produce very competitive results. Fastimplementation is also developed to reduce computational complexity. Extensiveexperiments on both simulated and real datasets demonstrate its robustness,effectiveness and efficiency.

Color Image and Multispectral Image Denoising Using Block Diagonal Representation