Abstract: A novel underwater image compositive enhancement method was proposed to improve the quality of underwater images, thereby synthetically boosting the performance of high-level visual analysis. A series of operations, including color compensation and correction, gamma correction in the HSV space, and final brightness de-blurring, were combined to realize color restoration, contrast and clarity improvements for underwater images. A method of brightness channel de-scattering based on Gauss filtering was proposed, and the comprehensive enhancement parameters of typical underwater images were analyzed. The experiments in this paper compared the processing results of the compositive enhancement method and other enhancement methods for the bluish, greenish, yellowish, and whitish nearshore shoal underwater images, and trained and tested the underwater image data sets enhanced by seven algorithms through the target detection network. Comparisons were also made between the average underwater target recognition accuracy rate and the ratio of the number of detected targets to the actual target number, so as to evaluate the effect of each enhancement algorithm on underwater target recognition and detection tasks. The experiment results demonstrate that the proposed method can achieve substantial image clarity improvement and color restoration, and is widely applicable, compared with the existing methods. At the same time, it can effectively improve the accuracy of underwater target recognition and the number of the detected objects. 

Key words: underwater image enhancement, Gauss filtering, brightness channel de-scattering, object detection, underwater image quality evaluation