Sujit Kumar Sahoo

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—Recent dictionary training algorithms for sparse representation like K-SVD, MOD, and their variation are reminiscent of K-means clustering, and this letter investigates such algorithms from that viewpoint. It shows: though K-SVD is sequential like K-means, it fails to simplify to K-means by destroying the structure in the sparse coefficients. In contrast,(More)
—Existing image denoising frameworks via sparse representation using learned dictionaries have an weakness that the dictionary, trained from noisy image, suffers from noise incursion. This paper analyzes this noise incursion, explicitly derives the noise component in the dictionary update step, and provides a simple remedy for a desired signal to noise(More)
—In Distributed Compressive Sensing (DCS), correlated sparse signals refer to as an ensemble of signals characterized by presenting a sparse correlation. If one signal is known apriori, the remaining signals in the ensemble can be reconstructed using l1-minimization with far fewer measurements compared to separate CS reconstruction. Reconstruction of such(More)
In this paper the problem of image restoration (denoising and inpainting) is approached using sparse approximation of local image blocks. The local image blocks are extracted by sliding square windows over the image. An adaptive block size selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image.(More)
Fusion based Compressive Sensing (CS) reconstruction algorithms combine multiple CS reconstruction algorithms, which worked with different principles, to obtain a better signal estimate. Examples include Fusion of Algorithms for Compressed Sensing (FACS) and Committee Machine Approach for Compressed Sensing (CoMACS). However, these algorithms involve(More)
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