UNDERWATER IMAGE ENHANCEMENT BASED ON STRUCTURE-TEXTURE DECOMPOSITION

被引:0
|
作者
Yang, Jingyu [1 ]
Wang, Xinyan [1 ]
Yue, Huanjing [1 ]
Fu, Xiaomei [2 ]
Hou, Chunping [1 ]
机构
[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China
[2] Tianjin Univ, Sch Marine Sci & Technol, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
Underwater images; color correction; structure-texture decomposition; contrast enhancement; de-noising;
D O I
暂无
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
Underwater images generally suffer from low contrast, serious noise and color distortion. The main challenges of underwater image enhancement are to preserve details in dark regions while avoiding oversaturetion in bright regions. This paper proposes a novel underwater image enhancement method based on image decomposition. By decomposing the high-frequency texture and noise into the texture layer, the transmission map is estimated from the noise-free structure layer to avoid the noise amplification problem in underwater image enhancement. Both the structure layer and texture layer are descattered with the estimated transmission map. After denoising by gradient residual minimizition, the texture layer is enhanced and added back into the structure layer to recover the final enhanced image. Experimental results verify that the proposed approach can recover the high-quality images with fine details and edges while improving contrast and color naturalness, especially for images taken in the high turbidity environment.
引用
收藏
页码:1207 / 1211
页数:5
相关论文
共 50 条
  • [31] Detecting Motion using the Structure-Texture Image Decomposition and Space-Time Interest Points
    Bellamine, I.
    Tairi, H.
    2013 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS: THEORIES AND APPLICATIONS (SITA), 2013,
  • [32] Memorizing Structure-Texture Correspondence for Image Anomaly Detection
    Zhou, Kang
    Li, Jing
    Xiao, Yuting
    Yang, Jianlong
    Cheng, Jun
    Liu, Wen
    Luo, Weixin
    Liu, Jiang
    Gao, Shenghua
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (06) : 2335 - 2349
  • [33] An Approach to Underwater Image Enhancement Based on Image Structural Decomposition
    JI Tingting
    WANG Guoyu
    Journal of Ocean University of China, 2015, 14 (02) : 255 - 260
  • [34] An approach to underwater image enhancement based on image structural decomposition
    Tingting Ji
    Guoyu Wang
    Journal of Ocean University of China, 2015, 14 : 255 - 260
  • [35] An approach to underwater image enhancement based on image structural decomposition
    Ji Tingting
    Wang Guoyu
    JOURNAL OF OCEAN UNIVERSITY OF CHINA, 2015, 14 (02) : 255 - 260
  • [36] Structure-texture image decomposition using a new non-local TV-Hilbert model
    Lv, Yehu
    IET IMAGE PROCESSING, 2020, 14 (11) : 2525 - 2531
  • [37] Underwater calibration image enhancement based on image block decomposition and fusion
    Chang, Zhi-wen
    Wang, Li-zhong
    Liang, Jin
    Li, Zhuang-zhuang
    Gong, Chun-yuan
    Wu, Zhi-hui
    Xu, Jian-ning
    CHINESE OPTICS, 2024, 17 (04) : 810 - 822
  • [38] Image Restoration Based on Structure and Texture Decomposition
    Zhang, Qiong
    Shen, Minfen
    Li, Bin
    PROCEEDINGS OF THE 2019 IEEE 18TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC 2019), 2019, : 217 - 221
  • [39] Super-resolution image visual quality assessment based on structure-texture features
    Zhou, Fei
    Sheng, Wei
    Lu, Zitao
    Kang, Bo
    Chen, Mianyi
    Qiu, Guoping
    SIGNAL PROCESSING-IMAGE COMMUNICATION, 2023, 117
  • [40] Fusion-based underwater image enhancement by wavelet decomposition
    Wang, Yafei
    Ding, Xueyan
    Wang, Ruoqian
    Zhang, Jun
    Fu, Xianping
    2017 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2017, : 1013 - 1018