{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T12:50:14Z","timestamp":1763988614776,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T00:00:00Z","timestamp":1745107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["12275142"],"award-info":[{"award-number":["12275142"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Omnidirectional image super-resolution (ODISR) is critical for VR\/AR applications, as high-quality 360\u00b0 visual content significantly enhances immersive experiences. However, existing ODISR methods suffer from limited receptive fields and high computational complexity, which restricts their ability to model long-range dependencies and extract global structural features. Consequently, these limitations hinder the effective reconstruction of high-frequency details. To address these issues, we propose a novel Mamba-based ODISR network, termed MambaOSR, which consists of three key modules working collaboratively for accurate reconstruction. Specifically, we first introduce a spatial-frequency visual state space model (SF-VSSM) to capture global contextual information via dual-domain representation learning, thereby enhancing the preservation of high-frequency details. Subsequently, we design a distortion-guided module (DGM) that leverages distortion map priors to adaptively model geometric distortions, effectively suppressing artifacts resulting from equirectangular projections. Finally, we develop a multi-scale feature fusion module (MFFM) that integrates complementary features across multiple scales, further improving reconstruction quality. Extensive experiments conducted on the SUN360 dataset demonstrate that our proposed MambaOSR achieves a 0.16 dB improvement in WS-PSNR and increases the mutual information by 1.99% compared with state-of-the-art methods, significantly enhancing both visual quality and the information richness of omnidirectional images.<\/jats:p>","DOI":"10.3390\/e27040446","type":"journal-article","created":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T20:31:36Z","timestamp":1745181096000},"page":"446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MambaOSR: Leveraging Spatial-Frequency Mamba for Distortion-Guided Omnidirectional Image Super-Resolution"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1902-9201","authenticated-orcid":false,"given":"Weilei","family":"Wen","sequence":"first","affiliation":[{"name":"VCIP, College of Computer Science, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7405-3349","authenticated-orcid":false,"given":"Qianqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"VCIP, College of Computer Science, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuli","family":"Shao","sequence":"additional","affiliation":[{"name":"VCIP, College of Computer Science, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Deng, X., Wang, H., Xu, M., Guo, Y., Song, Y., and Yang, L. 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