Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
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A4 Artikkeli konferenssijulkaisussa
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Date
2023-03-07
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Mcode
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Language
en
Pages
5
872-876
872-876
Series
56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022, Conference Record - Asilomar Conference on Signals, Systems and Computers, Volume 2022-October
Abstract
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.Description
Publisher Copyright: © 2022 IEEE.
Keywords
convolutional sparse coding, dictionary learning, image fusion, Simultaneous sparse approximation
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Citation
Veshki, F G & Vorobyov, S A 2023, Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion . in M B Matthews (ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 . Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2022-October, IEEE, pp. 872-876, Asilomar Conference on Signals, Systems, and Computers, Virtual, Online, United States, 31/10/2022 . https://doi.org/10.1109/IEEECONF56349.2022.10052057
