论文
论文标题: Deep images enhancement for turbid underwater images based on unsupervised learning
作者: Zhou, Wen-Hui; Zhu, Deng-Ming; Shi, Min; Li, Zhao-Xin; Duan, Ming; Wang, Zhao-Qi; Zhao, Guo-Liang; Zheng, Cheng-Dong
出版刊物: COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期: NOV
出版年份: 2022
卷/期:
DOI: 10.1016/j.compag.2022.107372
论文摘要: In agriculture, aquaculture technologies such as precise feeding, fish identification and fishing based on underwater machine vision all rely on the analysis of underwater images. However, due to the scatting and attenuation of the illumination in the real-world underwater environment, turbid underwater images are inevitably degraded, limiting their applicability in many vision tasks. In this paper, we present an unsupervised deep learning framework, called Underwater Loop Enhancement Network (ULENet), to improve the quality of turbid underwater images. We first propose an underwater dataset construction scheme and construct the dataset on which the network proposed above is trained. The underwater dataset contains images of three different scenes: lake and reservoir scene data (no label), pool scene data (weakly correlated label), and laboratory scene data (strongly correlated label). Then we propose a loop enhancement structure that uses the approximate candidates as labels and improves the visual quality of the image through the iterative training process. We formulate a new underwater visual perception loss function that evaluates the perceptual image quality based on its color, contrast, saturation and clarity. During the training process, a more realistic, higher -contrast, and clearer underwater image is gradually generated. Qualitative and quantitative evaluations show that the proposed method can effectively enhance image clarity. Moreover, the enhanced images are applied to several vision tasks to achieve better results, such as edge detection, key point matching, fish target detection and saliency prediction etc.
== 实验室与学会 ==
  • == 实验室与学会 ==
  • 水产品种创制与高效养殖全国重点实验室
  • 中国科学院藻类生物学重点实验室
  • 农业部淡水养殖病害防治重点实验室
  • 武汉白暨豚保护基金会
  • 湖北省海洋湖沼学会
  • 中国动物学会原生动物学分会
  • 中国动物学会斑马鱼分会
  • 湖北省暨武汉动物学会
  • 中国水产学会鱼病学专业委员会
  • 中国鱼类学会
== 平台建设 ==
  • == 平台建设 ==
  • “一带一路”海域赤潮数据库
  • 国家水生生物种质资源库
  • 国家斑马鱼资源中心
  • 中国科学院淡水藻种库
  • 中国科学院武汉生命科学大型仪器区域中心
  • 湿地生态系统观测研究野外站联盟
  • 中国科学院水生生物研究所分析测试中心
  • 中国科学院超级计算武汉分中心
  • 水生生物博物馆
== 相关网站推荐 ==
  • == 相关网站推荐 ==
  • 中国科学院
  • 农业农村部
  • 科学技术部
  • 生态环境部
  • 国家自然科学基金委员会
  • 中国科学院武汉分院
  • 湖北省科学技术厅
  • 湖北省生态环境厅
  • 湖北省农业农村厅