{"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/spatially-varying-blur-detection-based-on","title":"Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes","arxiv_id":"1703.07478","date":"2017-03-22","proceeding":"CVPR 2017 7","authors":["S. Alireza Golestaneh","Lina J. Karam"],"abstract":"The detection of spatially-varying blur without having any information about\nthe blur type is a challenging task. In this paper, we propose a novel\neffective approach to address the blur detection problem from a single image\nwithout requiring any knowledge about the blur type, level, or camera settings.\nOur approach computes blur detection maps based on a novel High-frequency\nmultiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The\nevaluations of the proposed approach on a diverse set of blurry images with\ndifferent blur types, levels, and contents demonstrate that the proposed\nalgorithm performs favorably against the state-of-the-art methods qualitatively\nand quantitatively.","url_abs":"http://arxiv.org/abs/1703.07478v3","url_pdf":"http://arxiv.org/pdf/1703.07478v3.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":"spatially-varying-blur-detection-based-on","repo_url":"https://github.com/Utkarsh-Deshmukh/Spatially-Varying-Blur-Detection-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.07478","atlas_url":"https://app.syntology.ai/?focus=1703.07478","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}