Underwater fish image enhancement method based on color correction

Abstract

Due to the absorption and scattering of light propagation underwater, the captured underwater images often have problems such as color bias, low contrast and poor clarity, resulting in low accuracy of underwater fish identification. To address this problem, this paper proposes an underwater fish image enhancement method based on color correction to enhance the acquired fish images and improve the accuracy of fish target recognition. Firstly, color correction is achieved by stretching the L component and changing the a and b components of the CIE-Lab color space to improve image sharpness. Then the colors of the R-G-B channels of the image are equalized to reduce the color bias. Finally, the histograms of the three R-G-B channels are redistributed. Comparison experiments were conducted with existing methods on a self-built fish image dataset, and the enhanced images were analyzed from both subjective and objective evaluations. The results showed that the enhancement effect of the method in this paper is better than other methods. Finally, the comparison experiments of target recognition before and after image enhancement were conducted on YOLOv5, and the results showed that the enhanced image target recognition accuracy was 99.8%, which was 1.2 percentage points higher than that before enhancement; the average accuracy mAP was 94.5%, which was 5.6 percentage points higher than that before image enhancement. The method in this paper can effectively improve the problems of underwater images and provide technical support for underwater target recognition.