Underwater Image Enhancement by Combining LAB Color Space and Double-Opponency Mechanism

Abstract

The captured underwater images often have color distortion and blur due to the propagation characteristics of light in water. The enhancement for underwater images using deep learning has shown promising prospect. However, most neural networks model the inputs and outputs end-to-end directly, which fails to fully utilize the inherent information present in underwater images. We focus more on the real color distribution in water that contains comprehensive information, and fully leverage these information to restore the underwater images without the color deviation. In this work, an underwater image enhancement model combining Lab color space and double-opponency mechanism is proposed. Lab color space can extract luminance and chroma, which can express wider chroma range based on human visual perception. Double-opponent mechanism can extract color constancy features based on the biological vision mechanism when the lights have attenuation differences. Moreover, an adaptive light estimation module is designed to learn the light map from the outputs of double-opponency to adjust the overall color of the images. Extensive experiments demonstrate that our approach achieves outstanding results in enhancing color and clarity in underwater images.