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Appl."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Since spatial-domain information has been widely exploited, there is a new trend to involve frequency-domain information in SR tasks. Besides, image SR is typically application-oriented and various computer vision tasks call for image arbitrary magnification. Therefore, in this article, we study image features in the frequency domain to design a novel image arbitrary-scale SR network. First, we statistically analyze LR-HR image pairs of several datasets under different scale factors and find that the high-frequency spectra of different images under different scale factors suffer from different degrees of degradation, but the valid low-frequency spectra tend to be retained within a certain distribution range. Then, based on this finding, we devise an adaptive scale-aware feature division mechanism using deep reinforcement learning, which can accurately and adaptively divide the frequency spectrum into the low-frequency part to be retained and the high-frequency one to be recovered. Finally, we design a scale-aware feature recovery module to capture and fuse multi-level features for reconstructing the high-frequency spectrum at arbitrary scale factors. Extensive experiments on public datasets show the superiority of our method compared with state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3616376","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T12:14:25Z","timestamp":1692188065000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["An Image Arbitrary-Scale Super-Resolution Network Using Frequency-domain Information"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4767-4817","authenticated-orcid":false,"given":"Jing","family":"Fang","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0257-5081","authenticated-orcid":false,"given":"Yinbo","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Northwestern Polytechnical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9796-488X","authenticated-orcid":false,"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9541-3021","authenticated-orcid":false,"given":"Xin","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer and Data Science, Ningbo Tech University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5872-3872","authenticated-orcid":false,"given":"Ruimin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.150"},{"key":"e_1_3_1_3_2","first-page":"2694","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Behjati Parichehr","year":"2021","unstructured":"Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Carles Fernandez Tena, and Jordi Gonzalez. 2021. 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