Underwater image restoration via backscatter pixel prior and color compensation. (May 2022)

• Record Type: Journal Article
• Authors: Zhou, Jingchun; Yang, Tongyu; Chu, Weishen; Zhang, Weishi
• Is Part Of: Engineering applications of artificial intelligence. Volume 111(2022)
• Publication Date: 2022-05
• Subjects: Underwater image -- Backscatter removal -- Color compensation -- Color correction -- Image restoration; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104785
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The use of underwater cameras instead of divers to enter complex underwater areas for real-time monitoring of fish, shrimp, and algae is significant to the aquaculture industry. However, underwater images are severely degraded due to light absorption and scattering, limiting the development of underwater computer vision and robot vision perception. To solve blurriness and color degradation issues, this paper developed a restoration method based on backscatter pixel prior and color cast removal from the physical point of view of underwater image degradation. The proposed method used only a single underwater image as an input to estimate various parameters accurately, such as depth map, backscatter map, and illuminant map. Specifically, a backscatter estimation algorithm based on a depth map was proposed to improve the contrast of underwater images. Then, an algorithm was designed to remove color deviation based on the illuminant map. In particular, a color compensation strategy was created that could completely eliminate red artifacts in underwater images that were generated by the strong attenuation of the red channel. We designed comparative experiments from multiple angles on different real underwater image datasets. Experiments showed that the proposed method improved the contrast and removed the color deviation of light absorption compared to several reported methods. Even on underwater images with severe attenuation, the proposed method showed a significant positive effectiveness and stability on color cast removal.