{"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/fast-and-effective-l0-gradient-minimization","title":"Fast and Effective L0 Gradient Minimization by Region Fusion","arxiv_id":null,"date":"2015-12-01","proceeding":"ICCV 2015 12","authors":["Rang M. H. Nguyen","Michael S. Brown"],"abstract":"L_0 gradient minimization can be applied to an input signal to control the number of non-zero gradients.  This is useful in reducing small gradients generally associated with signal noise, while preserving important signal features.  In computer vision, L_0 gradient minimization has found applications in image denoising, 3D mesh denoising, and image enhancement.  Minimizing the L_0 norm, however, is an NP-hard problem because of its non-convex property. As a result, existing methods rely on approximation strategies to perform the minimization.  In this paper, we present a new method to perform L_0 gradient minimization that is fast and effective. Our method uses a descent approach based on region fusion that converges faster than other methods while providing a better approximation of the optimal L_0 norm.  In addition, our method can be applied to both 2D images and 3D mesh topologies.  The effectiveness of our approach is demonstrated on a number of examples.","url_abs":"http://openaccess.thecvf.com/content_iccv_2015/html/Nguyen_Fast_and_Effective_ICCV_2015_paper.html","url_pdf":"http://openaccess.thecvf.com/content_iccv_2015/papers/Nguyen_Fast_and_Effective_ICCV_2015_paper.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":"fast-and-effective-l0-gradient-minimization","repo_url":"https://github.com/rangnguyen/L0-gradient-minimization","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-mesh-denoising","task_name":"3D Mesh Denoising"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}