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A simplified exposition of sparsity inducing penalty functions for denoising

Publication Type : Journal Article

Publisher : Springer

Source : ISTA, Springer, Vol 530, pp 1005-1015, 2016 (Scopus)

Url :

Campus : Coimbatore

School : School of Engineering

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Year : 2016

Abstract : This paper attempts to provide a pedagogical view to the approach of denoising using non-convex regularization developed by Ankit Parekh et al. The present paper proposes a simplified signal denoising approach by explicitly using sub-band matrices of decimated wavelet transform matrix. The objective function involves convex and non-convex terms in which the convexity of the overall function is restrained by parameterized non-convex term. The solution to this convexoptimization problem is obtained by employing Majorization-Minimization iterative algorithm. For the experimentation purpose, different wavelet filters such as daubechies, coiflets and reverse biorthogonal were used. © Springer International Publishing AG 2016.

Cite this Research Publication : Shivkaran Sing, Sachin Kumar S, KP Soman, A Simplified Exposition of Sparsity Inducing Penalty Functions for Denoising, ISTA, Springer, Vol 530, pp 1005-1015, 2016 (Scopus)

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