Contrast Enhancement via Dual Graph Total Variation-Based Image Decomposition

被引:12
|
作者
Liu, Xianming [1 ,2 ]
Zhai, Deming [1 ,2 ]
Bai, Yuanchao [3 ]
Ji, Xiangyang [4 ]
Gao, Wen [3 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
[2] Peng Cheng Lab, Shenzhen 518055, Peoples R China
[3] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China
[4] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
基金
美国国家科学基金会; 国家自然科学基金国际合作与交流项目;
关键词
Image decomposition; Noise reduction; Brightness; Image edge detection; Optimization; Lighting; Additives; Contrast enhancement; image denoising; image decomposition; graph signal modeling; graph total variation; FRAMEWORK; RETINEX;
D O I
10.1109/TCSVT.2019.2924454
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Images captured in low lighting environment suffer from both low luminance contrast and noise corruption. However, most existing contrast enhancement algorithms only consider contrast boosting, which tends to reveal or amplify noise that is originally not visible in the dark areas. In this paper, we propose a joint contrast enhancement and denoising algorithm, which is based on structure/texture layer decomposition via minimization of dual forms of graph total variation (GTV). Specifically, the structure layer is expected to be generally smoothing but with sharp edges at the foreground background boundaries, for which we propose a quadratic form of GTV (QGTV) as the prior that promotes signal smoothness along graph structure. For the texture layer, a re-weighted GTV (RGTV) is tailored to noise removal while preserving true image details. We provide theoretical analysis about the filtering behavior of these two priors. Furthermore, a boost factor is derived per patch via optimal contrast-tone mapping to improve the overall brightness level of the patch. Finally, an optimization objective function is formulated, which casts image decomposition, brightness boosting, and noise reduction into a unified optimization framework. We further propose a fast approach to efficiently solve the optimization and provide analysis about the convergency. The experimental results show that the proposed method outperforms the state-of-the-art works in subjective, objective, and statistical quality evaluation.
引用
收藏
页码:2463 / 2476
页数:14
相关论文
共 50 条
  • [21] Some Remarks on the Staircasing Phenomenon in Total Variation-Based Image Denoising
    Jalalzai, Khalid
    JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2016, 54 (02) : 256 - 268
  • [22] Efficient Iterative Regularization Method for Total Variation-Based Image Restoration
    Ma, Ge
    Yan, Ziwei
    Li, Zhifu
    Zhao, Zhijia
    ELECTRONICS, 2022, 11 (02)
  • [23] Independent Gabor analysis of multiscale total variation-based quotient image
    An, Gaoyun
    Wu, Jiying
    Ruan, Qiuqi
    IEEE SIGNAL PROCESSING LETTERS, 2008, 15 : 186 - 189
  • [24] ROBUST CONTRAST ENHANCEMENT VIA GRAPH-BASED CARTOON-TEXTURE DECOMPOSITION
    Zhai, Deming
    Liu, Xianming
    Ji, Xiangyang
    Bai, Yuanchao
    Zhao, Debin
    Gao, Wen
    2018 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2018,
  • [25] Some Remarks on the Staircasing Phenomenon in Total Variation-Based Image Denoising
    Khalid Jalalzai
    Journal of Mathematical Imaging and Vision, 2016, 54 : 256 - 268
  • [26] Image Quality Comparison of Reconstruction Using Total Variation-Based Regularizers
    Zhang, Jiahan
    Li, Si
    Lipson, Edward
    Schmidtlein, C. Ross
    Feiglin, David
    Xu, Yuesheng
    Krol, Andrzej
    2014 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2014,
  • [27] Total variation-based image noise reduction with generalized fidelity function
    Lee, Suk-Ho
    Kang, Moon Gi
    IEEE SIGNAL PROCESSING LETTERS, 2007, 14 (11) : 832 - 835
  • [28] Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation
    Cai M.
    Jiang J.
    Cai W.
    Zhou F.
    Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2024, 51 (02): : 157 - 169
  • [29] Improved total variation-based CT image reconstruction applied to clinical data
    Ritschl, Ludwig
    Bergner, Frank
    Fleischmann, Christof
    Kachelriess, Marc
    PHYSICS IN MEDICINE AND BIOLOGY, 2011, 56 (06): : 1545 - 1561
  • [30] Variation-based approach to image segmentation
    Yongping Zhang
    Nanning Zheng
    Rongchun Zhao
    Science in China Series : Information Sciences, 2001, 44 (4): : 259 - 269