{"ID":2875895,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01410","arxiv_id":"2509.01410","title":"A James-Stein Estimator based Generalized OMP Algorithm for Robust Signal Recovery using Sparse Representation","abstract":"In this paper, we introduce a novel algorithm named JS-gOMP, which enhances the generalized Orthogonal Matching Pursuit (gOMP) algorithm for improved noise robustness in sparse signal processing. The JS-gOMP algorithm uniquely incorporates the James-Stein estimator, optimizing the trade-off between signal recovery and noise suppression. This modification addresses the challenges posed by noise in the dictionary, a common issue in sparse representation scenarios. Comparative analyses demonstrate that JS-gOMP outperforms traditional gOMP, especially in noisy environments, offering a more effective solution for signal and image processing applications where noise presence is significant.","short_abstract":"In this paper, we introduce a novel algorithm named JS-gOMP, which enhances the generalized Orthogonal Matching Pursuit (gOMP) algorithm for improved noise robustness in sparse signal processing. The JS-gOMP algorithm uniquely incorporates the James-Stein estimator, optimizing the trade-off between signal recovery and...","url_abs":"https://arxiv.org/abs/2509.01410","url_pdf":"https://arxiv.org/pdf/2509.01410v1","authors":"[\"Debraj Banerjee\",\"Amitava Chatterjee\"]","published":"2025-09-01T12:07:44Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"math.ST\"]","methods":"[]","has_code":false}