{"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/a-matrix-in-matrix-neural-network-for-image","title":"A Matrix-in-matrix Neural Network for Image Super Resolution","arxiv_id":"1903.07949","date":"2019-03-19","proceeding":null,"authors":["Hailong Ma","Xiangxiang Chu","Shaohua Wan","Bo Zhang"],"abstract":"In recent years, deep learning methods have achieved impressive results with\nhigher peak signal-to-noise ratio in single image super-resolution (SISR) tasks\nby utilizing deeper layers. However, their application is quite limited since\nthey require high computing power. In addition, most of the existing methods\nrarely take full advantage of the intermediate features which are helpful for\nrestoration. To address these issues, we propose a moderate-size SISR net work\nnamed matrixed channel attention network (MCAN) by constructing a matrix\nensemble of multi-connected channel attention blocks (MCAB). Several models of\ndifferent sizes are released to meet various practical requirements.\nConclusions can be drawn from our extensive benchmark experiments that the\nproposed models achieve better performance with much fewer multiply-adds and\nparameters. Our models will be made publicly available.","url_abs":"http://arxiv.org/abs/1903.07949v1","url_pdf":"http://arxiv.org/pdf/1903.07949v1.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":"a-matrix-in-matrix-neural-network-for-image","repo_url":"https://github.com/macn3388/MCAN","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}