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King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In multispectral image compression, Convolutional Neural Network (CNN) effectively capture local features but are limited in modeling global context. Transformer architectures address this limitation by leveraging self-attention to model global dependencies, while the Mamba architecture\u2013built upon state space model (SSM)\u2013further enhances long-range sequence modeling. However, existing methods fail to effectively integrate the strengths of these approaches. To overcome this, we propose MSC-NET, a unified fusion network that combines Mamba-based sequence modeling with cross-channel aggregation Transformers. MSC-NET incorporates a Spectral Redundancy Elimination Attention (SREA) module to reduce spectral redundancy and an RMamba block to enhance global contextual representation. Additionally, a Cross-Channel Aggregation (CCA) module integrates the outputs of SREA and RMamba, improving the fusion of local features and inter-channel dependencies. Experimental results show that MSC-NET achieves superior rate-distortion performance, effectively balancing compression ratio and image quality, thereby validating the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1007\/s44443-025-00220-1","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:40:12Z","timestamp":1755765612000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mamba and cross-channel aggregation for efficient multispectral image compression"],"prefix":"10.1007","volume":"37","author":[{"given":"Jingang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7759-7518","authenticated-orcid":false,"given":"Qizhi","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"220_CR1","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arxiv:1607.06450"},{"key":"220_CR2","doi-asserted-by":"publisher","first-page":"11724","DOI":"10.1016\/j.image.2024.117247","volume":"132","author":"J Bacca","year":"2025","unstructured":"Bacca J, Arcos C, Ramirez JM et al (2025) Middle-output deep image prior for blind hyperspectral and multispectral image fusion. 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