{"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/revitalizing-convolutional-network-for-image","title":"Revitalizing Convolutional Network for Image Restoration","arxiv_id":null,"date":"2024-06-25","proceeding":"IEEE Transactions on Pattern Analysis and Machine Intelligence 2024 6","authors":["Yuning Cui","Wenqi Ren","Xiaochun Cao","Alois Knoll"],"abstract":"Image restoration aims to reconstruct a high-quality image from its corrupted version, playing essential roles in many scenarios. Recent years have witnessed a paradigm shift in image restoration from convolutional neural networks (CNNs) to Transformerbased models due to their powerful ability to model long-range pixel interactions. In this paper, we explore the potential of CNNs for image restoration and show that the proposed simple convolutional network architecture, termed ConvIR, can perform on par with or better than the Transformer counterparts. By re-examing the characteristics of 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 our ConvIR delivers state-ofthe- art performance with low computation complexity among 20 benchmark datasets on five representative image restoration tasks, including image dehazing, image motion/defocus deblurring, image deraining, and image desnowing.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10571568","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10571568","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":"revitalizing-convolutional-network-for-image","repo_url":"https://github.com/c-yn/ConvIR","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"},{"task_slug":"snow-removal","task_name":"Snow 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":"ConvIR","rank_in_archive_order":6,"of":12,"metrics":{"Average PSNR":"34.06"},"uses_additional_data":false},{"leaderboard":"/sota/image-deblurring-on-gopro","task":"Image Deblurring","dataset":"GoPro","model":"ConvIR","rank_in_archive_order":26,"of":55,"metrics":{"PSNR":"33.28","SSIM":"0.963"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-haze4k","task":"Image Dehazing","dataset":"Haze4k","model":"ConvIR","rank_in_archive_order":3,"of":11,"metrics":{"PSNR":"34.50","SSIM":"0.99"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-i-haze","task":"Image Dehazing","dataset":"I-Haze","model":"ConvIR","rank_in_archive_order":3,"of":4,"metrics":{"PSNR":"22.44","SSIM":"0.887"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-o-haze","task":"Image Dehazing","dataset":"O-Haze","model":"ConvIR","rank_in_archive_order":2,"of":7,"metrics":{"PSNR":"25.36","SSIM":"0.780"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-sots-indoor","task":"Image Dehazing","dataset":"SOTS Indoor","model":"ConvIR","rank_in_archive_order":1,"of":34,"metrics":{"PSNR":"42.72","SSIM":"0.997"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-sots-outdoor","task":"Image Dehazing","dataset":"SOTS Outdoor","model":"ConvIR","rank_in_archive_order":5,"of":31,"metrics":{"PSNR":"39.42","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}