{"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-scale-deep-compressive-sensing-network-1","title":"Multi-Scale Deep Compressive Sensing Network","arxiv_id":null,"date":"2018-12-12","proceeding":"Visual Communications and Image Processing Conference 2018 12","authors":["Canh","Thuong Nguyen; Jeon","Byeungwoo"],"abstract":"With joint learning of the sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. It is understood due to relatively much low-frequency information captured into the sampling matrix. This behaviour happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. This paper proposes a multi-scale DCS (MS-DCSNet) based on convolutional neural network. Firstly, we convert image signal using multiple scale-based wavelet transform. Then, the signal is captured through the convolution block by block across scales. The initial reconstructed image is directly recovered from multi-scale measurements. Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality. The network learns to perform both multi-scale in sampling and reconstruction thus results in better reconstruction quality.","url_abs":"https://arxiv.org/abs/1809.05717","url_pdf":"https://arxiv.org/abs/1809.05717","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-scale-deep-compressive-sensing-network-1","repo_url":"https://github.com/AtenaKid/MS-DCSNet-Release","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}