Learning Data Hallucination and Reciprocal Guidance for Underwater Depth Estimation and Color Correction

Biao Wu, Xinchen Ye, Fei Xue, Rui Xu

Abstract: Underwater vision is typically more difficult to tackle than open-air vision due to the degraded visibility and geometrical distortion, which impedes the development of underwater machine vision. Hence, we propose a joint depth estimation and color correction framework for underwater monocular images via data hallucination and reciprocal guidance learning. Specifically, due to the lack of labeled underwater data, we first design a data hallucination network to translate terrestrial images to multi-style synthetic underwater images while retaining the scene structure of terrestrial images from a single-source-multi-target perspective, benefiting the effective training of the joint tasks. Then, considering the strong connection between both tasks, we design a collaborative network to learn the reciprocal guidance between tasks from a multi-task perspective, thus improving the performance of each task. The whole framework can be trained end-to-end, and performs favorably against state-of-the-art methods in both depth estimation and color correction tasks.