Underwater Image Enhancement and Attenuation Restoration Based on Depth and Backscatter Estimation

Underwater image analytic technologies is important to study in-water imagery in oceanography. Due to the poor lighting conditions and severe scattering and attenuation of light, underwater image quality is heavily reduced in such environment. Therefore, underwater image enhancement has always been an essential step in the analysis pipeline. We develop an Underwater Image Enhancement and Attenuation Restoration (UIEAR) algorithm from a RGB image input based on 3D depth and backscatter estimation. The proposed underwater image enhancement method achieves superior performance with light computational requirements, making it easy to deploy on edge devices. We provide the following contributions: (1) Our image enhancement is based on depth estimation using a new smooth operator on RGB pixels, which provides 3D spatial information for improved backscatter estimation and attenuation restoration. (2) We develop an improved imaging model by considering parameters relative to the camera and the local light source to estimate the attenuation and the backscatter effects. Our light source estimation is constructed from a local neighborhood of pixels to avoid distortion of the backscatter and attenuation estimation. (3) We adopt white balance adjustment to enhance underwater pixels and better match real-world colors. Our method improves general underwater image analysis including object detection and segmentation. Experimental results demonstrate the effectiveness of our algorithm in restoring and enhancing underwater images.