{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/local-implicit-normalizing-flow-for-arbitrary","title":"Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution","arxiv_id":"2303.05156","date":"2023-03-09","proceeding":"CVPR 2023 1","authors":["Jie-En Yao","Li-Yuan Tsao","Yi-Chen Lo","Roy Tseng","Chia-Che Chang","Chun-Yi Lee"],"abstract":"Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose \"Local Implicit Normalizing Flow\" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.","url_abs":"https://arxiv.org/abs/2303.05156v3","url_pdf":"https://arxiv.org/pdf/2303.05156v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"local-implicit-normalizing-flow-for-arbitrary","repo_url":"https://github.com/JNNNNYao/LINF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"local-implicit-normalizing-flow-for-arbitrary","repo_url":"https://github.com/liyuantsao/BFSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"local-implicit-normalizing-flow-for-arbitrary","repo_url":"https://github.com/liyuantsao/flowsr-lp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"LINF","rank_in_archive_order":7,"of":21,"metrics":{"LPIPS":"0.112","PSNR":"27.33","SSIM":"0.76"},"uses_additional_data":true},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"LINF t=0.0","rank_in_archive_order":11,"of":21,"metrics":{"LPIPS":"0.248","PSNR":"29.14","SSIM":"0.83"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.05156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.05156"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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