Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 202, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2022.107372
Keywords
Image enhancement; Visual perception; Underwater dataset; Deep learning
Funding
- National Key R&D Program of China
- Scientific Research Instrument and Equipment Development Project of Chinese Academy of Sciences
- [2020YFB1710400]
- [YJKYYQ20190055]
Ask authors/readers for more resources
In this paper, we propose an unsupervised deep learning framework called Underwater Loop Enhancement Network (ULENet) to improve the quality of turbid underwater images. By using approximate candidates as labels and applying a loop enhancement structure, a more realistic, higher-contrast, and clearer underwater image is gradually generated. The proposed method effectively enhances image clarity and achieves better results in various vision tasks.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available