{"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/hinet-half-instance-normalization-network-for","title":"HINet: Half Instance Normalization Network for Image Restoration","arxiv_id":"2105.06086","date":"2021-05-13","proceeding":null,"authors":["Liangyu Chen","Xin Lu","Jie Zhang","Xiaojie Chu","Chengpeng Chen"],"abstract":"In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8 times and 2.9 times speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3 times speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4 times speedup. With HINet, we won 1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG Artifacts, with a PSNR of 29.70. The code is available at https://github.com/megvii-model/HINet.","url_abs":"https://arxiv.org/abs/2105.06086v2","url_pdf":"https://arxiv.org/pdf/2105.06086v2.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":"hinet-half-instance-normalization-network-for","repo_url":"https://github.com/megvii-model/HINet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"hinet-half-instance-normalization-network-for","repo_url":"https://github.com/akalia77/hinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"rain-removal","task_name":"Rain Removal"},{"task_slug":"single-image-deraining","task_name":"Single Image Deraining"},{"task_slug":"spectral-reconstruction","task_name":"Spectral Reconstruction"}],"methods":[{"method_slug":"instance-normalization","method_name":"Instance Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/deblurring-on-gopro","task":"Deblurring","dataset":"GoPro","model":"HINet","rank_in_archive_order":31,"of":56,"metrics":{"PSNR":"32.71"},"uses_additional_data":false},{"leaderboard":"/sota/image-denoising-on-sidd","task":"Image Denoising","dataset":"SIDD","model":"HINet","rank_in_archive_order":7,"of":22,"metrics":{"PSNR (sRGB)":"39.99","SSIM (sRGB)":"0.958"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-rain100h","task":"Single Image Deraining","dataset":"Rain100H","model":"HINet","rank_in_archive_order":8,"of":19,"metrics":{"PSNR":"30.65","SSIM":"0.894"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-rain100l","task":"Single Image Deraining","dataset":"Rain100L","model":"HINet","rank_in_archive_order":11,"of":19,"metrics":{"PSNR":"37.28","SSIM":"0.97"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test100","task":"Single Image Deraining","dataset":"Test100","model":"HINet","rank_in_archive_order":5,"of":12,"metrics":{"PSNR":"30.29","SSIM":"0.906"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test1200","task":"Single Image Deraining","dataset":"Test1200","model":"HINet","rank_in_archive_order":6,"of":14,"metrics":{"PSNR":"33.05","SSIM":"0.919"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test2800","task":"Single Image Deraining","dataset":"Test2800","model":"HINet","rank_in_archive_order":3,"of":12,"metrics":{"PSNR":"33.91","SSIM":"0.941"},"uses_additional_data":false},{"leaderboard":"/sota/spectral-reconstruction-on-arad-1k","task":"Spectral Reconstruction","dataset":"ARAD-1K","model":"HINet","rank_in_archive_order":6,"of":11,"metrics":{"MRAE":"0.2032","PSNR":"32.51","RMSE":"0.0303"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2105.06086","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}