{"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/full-image-recover-for-block-based","title":"Full Image Recover for Block-Based Compressive Sensing","arxiv_id":"1802.00179","date":"2018-02-01","proceeding":null,"authors":["Xuemei Xie","Chenye Wang","Jiang Du","Guangming Shi"],"abstract":"Recent years, compressive sensing (CS) has improved greatly for the\napplication of deep learning technology. For convenience, the input image is\nusually measured and reconstructed block by block. This usually causes block\neffect in reconstructed images. In this paper, we present a novel CNN-based\nnetwork to solve this problem. In measurement part, the input image is\nadaptively measured block by block to acquire a group of measurements. While in\nreconstruction part, all the measurements from one image are used to\nreconstruct the full image at the same time. Different from previous method\nrecovering block by block, the structure information destroyed in measurement\npart is recovered in our framework. Block effect is removed accordingly. We\ntrain the proposed framework by mean square error (MSE) loss function.\nExperiments show that there is no block effect at all in the proposed method.\nAnd our results outperform 1.8 dB compared with existing methods.","url_abs":"http://arxiv.org/abs/1802.00179v1","url_pdf":"http://arxiv.org/pdf/1802.00179v1.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":"full-image-recover-for-block-based","repo_url":"https://github.com/jiang-du/Perceptual-CS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}