Representative Color Transform for Image Enhancement
Hanul Kim, Su-Min Choi, Chang-Su Kim, Yeong Jun Koh
Abstract
Recently, the encoder-decoder and intensity transformation approaches lead to impressive progress in image enhancement. However, the encoder-decoder often loses details in input images during down-sampling and up-sampling processes. Also, the intensity transformation has a limited capacity to cover color transformation between low-quality and high-quality images. In this paper, we propose a novel approach, called representative color transform (RCT), to tackle these issues in existing methods. RCT determines different representative colors specialized in input images and estimates transformed colors for the representative colors. It then determines enhanced colors using these transformed colors based on the similarity between input and representative colors. Extensive experiments demonstrate that the proposed algorithm outperforms recent state-of-the-art algorithms on various image enhancement problems.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 15b91abd-63a9-4c91-953b-2f29e0cfc949Cited by top-tier papers19
- SNR-Aware Low-light Image EnhancementXiaogang Xu, Ruixing Wang, Chi-Wing Fu, Jiaya JiaCVPR 2022 · 552 citations
- Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip StrategyYi Tang, Hiroshi Kawasaki, Takafumi IwaguchiACM MM 2023 · 124 citations
- Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling NetworkYinglong Wang, Zhen Liu, Jianzhuang Liu, Songcen Xu et al.ICCV 2023 · 70 citations
- AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image EnhancementCanqian Yang, Meiguang Jin, Xu Jia, Yi Xu et al.CVPR 2022 · 57 citations
- A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene ReconstructionChongshan Lu, Fukun Yin, Xin Chen, Wen Liu et al.ICCV 2023 · 48 citations
Builds on6
- Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing SoftwareSatoshi Kosugi, Toshihiko YamasakiAAAI 2020 · 104 citations
- DeepLPF: Deep Local Parametric Filters for Image EnhancementSean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot et al.CVPR 2020
- Zero-Reference Deep Curve Estimation for Low-Light Image EnhancementChunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy et al.CVPR 2020
- Learning to Restore Low-Light Images via Decomposition-and-EnhancementKe Xu, Xin Yang, Baocai Yin, Rynson W. H. LauCVPR 2020
- EfficientDet: Scalable and Efficient Object DetectionMingxing Tan, Ruoming Pang, Quoc V. LeCVPR 2020
Related papers
- Color Transfer with Modulated FlowsMaria A. Larchenko, Alexander Lobashev, Dmitry Guskov, Vladimir Vladimirovich PalyulinAAAI 2025 · 9 citations
- Luminance-aware Color Transform for Multiple Exposure CorrectionJong-Hyeon Baek, Daehyun Kim, Su-Min Choi, Hyo-Jun Lee et al.ICCV 2023 · 17 citations
- High-Resolution Image Harmonization with Adaptive-Interval Color TransformationQuanling Meng, Qinglin Liu, Zonglin Li, Xiangyuan Lan et al.NeurIPS 2024 · 16 citations
- CLUT-Net: Learning Adaptively Compressed Representations of 3DLUTs for Lightweight Image EnhancementFengyi Zhang, Hui Zeng, Tianjun Zhang, Lin ZhangACM MM 2022 · 26 citations
- Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent CorrelationsJing Yang, Qunliang Xing, Mai Xu, Minglang QiaoICCV 2025
