{"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/fast-and-efficient-image-quality-enhancement","title":"Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV2018 2018 9","authors":["Thang Vu","Cao V. Nguyen","Trung X. Pham","Tung M. Luu","Chang D. Yoo"],"abstract":"This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution\r\nor image enhancement to provide accurate yet visually pleasing images\r\non mobile devices by addressing the following three main issues. First,\r\nthe considered FEQE performs majority of its computation in a lowresolution space. Second, the number of channels used in the convolutional layers is small which allows FEQE to be very deep. Third, the\r\nFEQE performs downsampling referred to as desubpixel that does not\r\nlead to loss of information. Experimental results on a number of standard\r\nbenchmark datasets show significant improvements in image fidelity and\r\nreduction in processing time of the proposed FEQE compared to the recent state-of-the-art methods. In the PIRM 2018 challenge, the proposed\r\nFEQE placed first on the image super-resolution task for mobile devices.","url_abs":"http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Vu_Fast_and_Efficient_Image_Quality_Enhancement_via_Desubpixel_Convolutional_Neural_ECCVW_2018_paper.pdf","url_pdf":"http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Vu_Fast_and_Efficient_Image_Quality_Enhancement_via_Desubpixel_Convolutional_Neural_ECCVW_2018_paper.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":"fast-and-efficient-image-quality-enhancement","repo_url":"https://github.com/thangvubk/FEQE","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}