{"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/convolutional-dictionary-learning-via-local","title":"Convolutional Dictionary Learning via Local Processing","arxiv_id":"1705.03239","date":"2017-05-09","proceeding":"ICCV 2017 10","authors":["Vardan Papyan","Yaniv Romano","Jeremias Sulam","Michael Elad"],"abstract":"Convolutional Sparse Coding (CSC) is an increasingly popular model in the\nsignal and image processing communities, tackling some of the limitations of\ntraditional patch-based sparse representations. Although several works have\naddressed the dictionary learning problem under this model, these relied on an\nADMM formulation in the Fourier domain, losing the sense of locality and the\nrelation to the traditional patch-based sparse pursuit. A recent work suggested\na novel theoretical analysis of this global model, providing guarantees that\nrely on a localized sparsity measure. Herein, we extend this local-global\nrelation by showing how one can efficiently solve the convolutional sparse\npursuit problem and train the filters involved, while operating locally on\nimage patches. Our approach provides an intuitive algorithm that can leverage\nstandard techniques from the sparse representations field. The proposed method\nis fast to train, simple to implement, and flexible enough that it can be\neasily deployed in a variety of applications. We demonstrate the proposed\ntraining scheme for image inpainting and image separation, while achieving\nstate-of-the-art results.","url_abs":"http://arxiv.org/abs/1705.03239v1","url_pdf":"http://arxiv.org/pdf/1705.03239v1.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":"convolutional-dictionary-learning-via-local","repo_url":"https://github.com/qu-arx/arx-inf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}