{"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/unsupervised-blur-kernel-estimation-and","title":"Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution","arxiv_id":null,"date":"2022-04-25","proceeding":"IEEE Access 2022 4","authors":["Youngsoo Kim","JEONGHYO HA","Yooshin Cho","Junmo Kim"],"abstract":"Blind super-resolution (blind-SR) is an important task in the field of computer vision and has\r\nvarious applications in real-world. Blur kernel estimation is the main element of blind-SR along with the\r\nadaptive SR networks and a more accurately estimated kernel guarantees a better performance. Recently,\r\ngenerative adversarial networks (GANs), comparing recurrence patches across scales, have been the most\r\nsuccessful unsupervised kernel estimation methods. However, they still involve several problems. ① Their\r\nsharpness discrimination ability has been noted as being too weak, causing them to focus more on pattern\r\nshapes than sharpness. ② In some cases, kernel correction processes were omitted; however, these are\r\nessential because the optimally generated kernel may be narrower than a point spread function (PSF)\r\nexcept when the PSF is ideal low-pass filter. ③ Previous studies also did not consider that GANs are\r\naffected by the thickness of edges as well as PSF. Thus, in this paper, 1) we propose a degradation and\r\nranking comparison process designed to induce GAN models to became sensitive to image sharpness,\r\nand 2) propose a scale-free kernel correction technique using Gaussian kernel approximation including a\r\nthickness parameter. To improve the kernel accuracy further, we 3) propose a combination model of the\r\nproposed GAN and DIP(deep image prior) for more supervision, and designed a kernel correction network\r\nto propagate gradients through developed correction method. Several experiments demonstrate that our\r\nmethods enhanced the l2 error and the shape of the kernel significantly. In addition, by combining with\r\nordinary blind-SR algorithms, the best reconstruction accuracy was achieved among unsupervised blur kernel\r\nestimation methods.","url_abs":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9762718","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9762718","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":"unsupervised-blur-kernel-estimation-and","repo_url":"https://github.com/ysook1m/Enhanced_KernelGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"blind-super-resolution","task_name":"Blind Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/blind-super-resolution-on-div2krk-2x","task":"Blind Super-Resolution","dataset":"DIV2KRK - 2x upscaling","model":"Enhanced-KernelGAN-DIP + ZSSR","rank_in_archive_order":4,"of":5,"metrics":{"PSNR":"31.62","SSIM":"0.8874"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}