Underwater Color Disparities: Cues for Enhancing Underwater Images Toward Natural Color Consistencies

We observe that a natural image tends to exhibit similar histograms for color channels in the RGB color space and consistent statistical estimates for color channels in the Lab color space. We refer to these observations as natural color consistencies. In contrast, we discover that an underwater image does not always follow the natural color consistencies. Different color channels in an underwater image tend to give rise to very different distributions, regardless of whether the channels are in the RGB color space or Lab color space. We refer to these observations as underwater color disparities. To enhance an underwater image to make it appear more natural, it is necessary to correct its underwater color disparities to align with the natural color consistencies. To this end, we develop an adaptive attenuated channel compensation method based on optimal channel precorrection and a salient absorption map-guided fusion method for eliminating the color deviation in the RGB color space. We then develop a method to enhance the contrast of channel L and an adaptive color distribution specification method for improving the contrast and matching the color distribution in the Lab color space. Additionally, we develop an edge-enhanced mask fusion method for correcting blurry details. Our method is not a deep learning method but can effectively be applied to a single underwater image. The qualitative and quantitative empirical results validate that our method outperforms state-of-the-art underwater image enhancement methods. We release the reproducible code at
https://gitee.com/wanghaoupc/Underwater_Color_Disparities
for public evaluation.