{"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/transition-constant-normalization-for-image","title":"Transition-constant Normalization for Image Enhancement","arxiv_id":null,"date":"2023-09-21","proceeding":"NeurIPS 2023 11","authors":[],"abstract":"Normalization techniques that capture image style by statistical representation have become a popular component in deep neural networks.\nAlthough image enhancement can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance. \nTo fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks.\nSpecifically, it consists of two streams of normalization operations arranged under an invertible constraint, along with a feature sub-sampling operation that satisfies the normalization constraint.\nTCN enjoys several merits, including being parameter-free, plug-and-play, and incurring no additional computational costs.\nWe provide various formats to utilize TCN for image enhancement, including seamless  integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures.\nThrough extensive experiments on multiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements.\nBesides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation.\nThe code is available at  \\textit{\\textcolor{blue}{https://github.com/huangkevinj/TCNorm}}.","url_abs":"https://openreview.net/forum?id=GEWzHeHpLr","url_pdf":"https://openreview.net/pdf?id=GEWzHeHpLr","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":"transition-constant-normalization-for-image","repo_url":"https://github.com/huangkevinj/tcnorm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}