@inproceedings{993bcf80033c49cb807004fc6d374827,
title = "Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion",
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.",
keywords = "convolutional sparse coding, dictionary learning, image fusion, Simultaneous sparse approximation",
author = "Veshki, {Farshad G.} and Vorobyov, {Sergiy A.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; Asilomar Conference on Signals, Systems, and Computers, ACSSC ; Conference date: 31-10-2022 Through 02-11-2022",
year = "2023",
month = mar,
day = "7",
doi = "10.1109/IEEECONF56349.2022.10052057",
language = "English",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE",
pages = "872--876",
editor = "Matthews, {Michael B.}",
booktitle = "56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022",
address = "United States",
}