{"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/efficient-single-image-super-resolution-using","title":"Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions","arxiv_id":"1808.08509","date":"2018-08-26","proceeding":null,"authors":["Vandit Jain","Prakhar Bansal","Abhinav Kumar Singh","Rajeev Srivastava"],"abstract":"Convolutional Neural Networks (CNNs) have demonstrated great results for the\nsingle-image super-resolution (SISR) problem. Currently, most CNN algorithms\npromote deep and computationally expensive models to solve SISR. However, we\npropose a novel SISR method that uses relatively less number of computations.\nOn training, we get group convolutions that have unused connections removed. We\nhave refined this system specifically for the task at hand by removing\nunnecessary modules from original CondenseNet. Further, a reconstruction\nnetwork consisting of deconvolutional layers has been used in order to upscale\nto high resolution. All these steps significantly reduce the number of\ncomputations required at testing time. Along with this, bicubic upsampled input\nis added to the network output for easier learning. Our model is named\nSRCondenseNet. We evaluate the method using various benchmark datasets and show\nthat it performs favourably against the state-of-the-art methods in terms of\nboth accuracy and number of computations required.","url_abs":"http://arxiv.org/abs/1808.08509v1","url_pdf":"http://arxiv.org/pdf/1808.08509v1.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":"efficient-single-image-super-resolution-using","repo_url":"https://github.com/vandit15/SRCondenseNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}