{"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/bidirectional-recurrent-convolutional","title":"Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution","arxiv_id":null,"date":"2015-12-01","proceeding":"NeurIPS 2015 12","authors":["Yan Huang","Wei Wang","Liang Wang"],"abstract":"Super resolving a low-resolution video is usually handled by either single-image super-resolution (SR) or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video super-resolution. Multi-Frame SR generally extracts motion information, e.g. optical flow, to model the temporal dependency, which often shows high computational cost. Considering that recurrent neural network (RNN) can model long-term contextual information of temporal sequences well, we propose a bidirectional recurrent convolutional network for efficient multi-frame SR.Different from vanilla RNN, 1) the commonly-used recurrent full connections are replaced with weight-sharing convolutional connections and 2) conditional convolutional connections from previous input layers to current hidden layer are added for enhancing visual-temporal dependency modelling. With the powerful temporal dependency modelling, our model can super resolve videos with complex motions and achieve state-of-the-art performance. Due to the cheap convolution operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame methods.","url_abs":"http://papers.nips.cc/paper/5778-bidirectional-recurrent-convolutional-networks-for-multi-frame-super-resolution","url_pdf":"http://papers.nips.cc/paper/5778-bidirectional-recurrent-convolutional-networks-for-multi-frame-super-resolution.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":[],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"multi-frame-super-resolution","task_name":"Multi-Frame Super-Resolution"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"temporal-sequences","task_name":"Temporal Sequences"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-super-resolution-on-vid4-4x-upscaling","task":"Video Super-Resolution","dataset":"Vid4 - 4x upscaling","model":"BRCN","rank_in_archive_order":23,"of":27,"metrics":{"PSNR":"24.43","SSIM":"0.662"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}