Underwater image enhancement method with light scattering characteristics. (May 2022)

• Record Type: Journal Article
• Authors: Zhou, Jingchun; Wei, Xiaojing; Shi, Jinyu; Chu, Weishen; Zhang, Weishi
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: Underwater image enhancement -- Light scattering -- Color restoration -- Detail loss; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107898
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Underwater image is divided into five categories. Calculate the color loss rate of RGB three channels. Multi scene color restoration method to correct the color. Stretch 64 multi contrast factor histograms to improve the contrast. Abstract: Light is absorbed and scattered when propagating in water, which results in low quality and poor visibility of underwater optical images. Furthermore, the absorption of light by water causes color distortion, whereas the scattering of light by small particles suspended in water results in low image contrast. To enhance the underwater image quality, we developed a method based on light scattering characteristics. Firstly, we group the color cast into five categories according to the proportion of the average of the RGB channels. Then, we use the optical attenuation characteristics to calculate the color loss rate of the RGB channels of underwater images in different scenes and develop a multi-scene color restoration method to correct the color cast of underwater images. While keeping the color constant, we set a 64-block multi-contrast factor histogram stretching to enhance the contrast of the underwater image. The experimental results verify that the developed method has achieved image quality improvement via qualitative and quantitative evaluation. Our approach is proved to effectively improve the quality of underwater images with color distortion, low contrast, and detail loss. Graphical abstract: Image, graphical abstract