Dual-color space color correction and histogram segmentation optimized strategy for underwater image enhancement

Underwater images are affected by light scattering and absorption in the water. It usually faces challenges such as color distortion, loss of details, and low contrast. To address the above problems, this paper proposes a dual-color space color correction and histogram segmentation optimized strategy for underwater image enhancement. Specifically, this paper first calculates the quantile for adjusting the pixel distribution and performs histogram stretching to correct the image's overall color. Then, a LAB color balancing strategy is designed to eliminate the color deviation resulting from the color correction process. Finally, histogram segmentation and adaptive pixel allocation methods are proposed to improve overall contrast. Experimental studies on three benchmark datasets for comparison with six state-of-the-art algorithms are conducted. Experimental results show the effectiveness of the mechanism proposed in this paper. Meanwhile, the proposed approach proves effective for key point and saliency detection. Additionally, the proposed approach exhibits promising results for images captured under challenging conditions such as low illumination, haze, and dust storms.