{"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/multi-focus-image-fusion-using-dictionary","title":"Multi-focus Image Fusion using dictionary learning and Low-Rank Representation","arxiv_id":"1804.08355","date":"2018-04-23","proceeding":null,"authors":["Hui Li","Xiao-Jun Wu"],"abstract":"Among the representation learning, the low-rank representation (LRR) is one\nof the hot research topics in many fields, especially in image processing and\npattern recognition. Although LRR can capture the global structure, the ability\nof local structure preservation is limited because LRR lacks dictionary\nlearning. In this paper, we propose a novel multi-focus image fusion method\nbased on dictionary learning and LRR to get a better performance in both global\nand local structure. Firstly, the source images are divided into several\npatches by sliding window technique. Then, the patches are classified according\nto the Histogram of Oriented Gradient (HOG) features. And the sub-dictionaries\nof each class are learned by K-singular value decomposition (K-SVD) algorithm.\nSecondly, a global dictionary is constructed by combining these\nsub-dictionaries. Then, we use the global dictionary in LRR to obtain the LRR\ncoefficients vector for each patch. Finally, the l_1-norm and choose-max fuse\nstrategy for each coefficients vector is adopted to reconstruct fused image\nfrom the fused LRR coefficients and the global dictionary. Experimental results\ndemonstrate that the proposed method can obtain state-of-the-art performance in\nboth qualitative and quantitative evaluations compared with serval classical\nmethods and novel methods.The Code of our fusion method is available at\nhttps://github.com/hli1221/imagefusion_dllrr","url_abs":"http://arxiv.org/abs/1804.08355v2","url_pdf":"http://arxiv.org/pdf/1804.08355v2.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":"multi-focus-image-fusion-using-dictionary","repo_url":"https://github.com/exceptionLi/imagefusion_dllrr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"multi-focus-image-fusion-using-dictionary","repo_url":"https://github.com/hli1221/imagefusion_dllrr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"multi-focus-image-fusion","task_name":"Multi Focus Image Fusion"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}