{"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/lyt-net-lightweight-yuv-transformer-based","title":"LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement","arxiv_id":"2401.15204","date":"2024-01-26","proceeding":null,"authors":["A. Brateanu","R. Balmez","A. Avram","C. Orhei","C. Ancuti"],"abstract":"This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement (LLIE). LYT-Net consists of several layers and detachable blocks, including our novel blocks--Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)--along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels U and V and luminance channel Y as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods. The source code and pre-trained models are available at https://github.com/albrateanu/LYT-Net","url_abs":"https://arxiv.org/abs/2401.15204v6","url_pdf":"https://arxiv.org/pdf/2401.15204v6.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":"lyt-net-lightweight-yuv-transformer-based","repo_url":"https://github.com/albrateanu/lyt-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lyt-net-lightweight-yuv-transformer-based","repo_url":"https://github.com/albrateanu/LYT-Net/tree/main/PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"color-image-denoising","task_name":"Color Image Denoising"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"}],"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":"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/low-light-image-enhancement-on-lol","task":"Low-Light Image Enhancement","dataset":"LOL","model":"LYT-Net","rank_in_archive_order":6,"of":40,"metrics":{"Average PSNR":"27.23","FLOPS (G)":"3.49","LPIPS":"0.071","Params (M)":"0.045","SSIM":"0.853"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-enhancement-on-lolv2","task":"Low-Light Image Enhancement","dataset":"LOLv2","model":"LYT-Net","rank_in_archive_order":9,"of":12,"metrics":{"Average PSNR":"27.80","LPIPS":"0.078","SSIM":"0.873"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-enhancement-on-lolv2-1","task":"Low-Light Image Enhancement","dataset":"LOLv2-synthetic","model":"LYT-Net","rank_in_archive_order":5,"of":9,"metrics":{"Average PSNR":"29.38","LPIPS":"0.037","SSIM":"0.939"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2401.15204","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}