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 this paper the problem of image inpainting is addressed using sparse approximation of local image patches. The small patches are extracted by sliding square windows. An adaptive window selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive window selection yields the(More)
Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. To effectively treat or prevent A-fib, automatic A-fib detection based on Electrocardiograph (ECG) monitoring is highly desirable. This paper reviews recently developed techniques for A-fib detection based on non-episodic surface ECG monitoring data. A-fib detection methods in the literature(More)
In this paper the problem of image denoising is approached using sparse approximation of local image patches. The small patches are extracted by sliding square windows. An adaptive window selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive window selection yields the(More)
We have recently proposed a Sequential Generalization of K-means (SGK) to train dictionary for sparse representation. SGK's training performance is as effective as the standard dictionary training algorithm K-SVD, alongside it has a simpler implementation to its advantage. In this piece of work, through the problem of image denoising, we are making a fair(More)
Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) are two well-known recovery algorithms in compressed sensing. To recover a d-dimensional m-sparse signal with high probability, OMP needs O(m ln d) number of measurements, whereas BP needs only O(m ln d/m) number of measurements. In contrary, OMP is a practically more appealing algorithm due to its(More)
In Distributed Compressive Sensing (DCS), correlated sparse signals stand for 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 l<sub>1</sub>-minimization with far fewer measurements compared to separate CS reconstruction. Reconstruction(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)