{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T10:31:33Z","timestamp":1774348293181,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \\( \\mathcal{O}(N^2d) \\) to \\( \\mathcal{O}(Nr^2) \\), with \\( r<\/jats:p>","DOI":"10.1609\/aaai.v40i13.38103","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:57Z","timestamp":1773792057000},"page":"11232-11240","source":"Crossref","is-referenced-by-count":0,"title":["TRT: Harnessing Tensor Ring Transformer for Hyperspectral Image Super-Resolution"],"prefix":"10.1609","volume":"40","author":[{"given":"Honghui","family":"Xu","sequence":"first","affiliation":[]},{"given":"Junwei","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yubin","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Yueqian","family":"Quan","sequence":"additional","affiliation":[]},{"given":"Chuangjie","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Jianwei","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38103\/42065","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38103\/48992","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38103\/42065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:28:40Z","timestamp":1774337320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i13.38103","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}