{"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/irnext-rethinking-convolutional-network","title":"IRNeXt: Rethinking Convolutional Network Design for Image Restoration","arxiv_id":null,"date":"2023-04-24","proceeding":"ICML 2023 4","authors":["Yuning Cui","Wenqi Ren","Sining Yang","Xiaochun Cao","Alois Knoll"],"abstract":"We present IRNeXt, a simple yet effective convolutional network architecture for image restoration. Recently, Transformer models have dominated the field of image restoration due to the powerful ability of modeling long-range pixels interactions. In this paper, we excavate the potential of the convolutional neural network (CNN) and show that our CNN-based model can receive comparable or better performance than Transformer models with low computation overhead on several image restoration tasks. By re-examining the characteristics possessed by advanced image restoration algorithms, we discover several key factors leading to the performance improvement of restoration models. This motivates us to develop a novel network for image restoration based on cheap convolution operators. Comprehensive experiments demonstrate that IRNeXt delivers state-of-the-art performance among numerous datasets on a range of image restoration tasks with low computational complexity, including image dehazing, single-image defocus/motion deblurring, image deraining, and image desnowing.","url_abs":"https://openreview.net/forum?id=MZkbgahv4a","url_pdf":"https://openreview.net/forum?id=MZkbgahv4a","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":"irnext-rethinking-convolutional-network","repo_url":"https://github.com/c-yn/IRNeXt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"},{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"rain-removal","task_name":"Rain Removal"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/deblurring-on-rsblur","task":"Deblurring","dataset":"RSBlur","model":"IRNext","rank_in_archive_order":5,"of":12,"metrics":{"Average PSNR":"34.08"},"uses_additional_data":false},{"leaderboard":"/sota/image-deblurring-on-gopro","task":"Image Deblurring","dataset":"GoPro","model":"IRNeXt","rank_in_archive_order":27,"of":55,"metrics":{"PSNR":"33.16","SSIM":"0.962"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-sots-indoor","task":"Image Dehazing","dataset":"SOTS Indoor","model":"IRNeXt","rank_in_archive_order":11,"of":34,"metrics":{"PSNR":"41.21","SSIM":"0.996"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-sots-outdoor","task":"Image Dehazing","dataset":"SOTS Outdoor","model":"IRNeXt","rank_in_archive_order":6,"of":31,"metrics":{"PSNR":"39.18","SSIM":"0.996"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}