UW-GAN: Single-Image Depth Estimation and Image Enhancement for Underwater Images

Abstract

Due to the unavailability of large scale underwater depth image datasets and ill-posed problems, underwater single image depth prediction is a challenging task. An unambiguous depth prediction for single underwater image is an essential part of applications like underwater robotics, marine engineering, etc. This paper presents an end-to-end Underwater Generative Adversarial Network (UW-GAN) for depth estimation from an underwater single image. Initially, a coarse-level depth map is estimated using the Underwater Coarse-level Generative Network (UWC-Net). Then, a fine-level depth map is computed using the Underwater Fine-level Network (UWF-Net) which takes input as the concatenation of the estimated coarse-level depth map and the input image. The proposed UWF-Net comprises of spatial and channel-wise squeeze and excitation block for fine-level depth estimation. Also, we propose a synthetic underwater image generation approach for large scale database. The proposed network is tested on real-world and synthetic underwater datasets for its performance analysis. We also perform a complete evaluation of the proposed UW-GAN on underwater images having different color domination, contrast, and lighting conditions. Presented UW-GAN framework is also investigated for underwater single image enhancement. Extensive result analysis proves the superiority of proposed UW-GAN over the state-of-the-art hand-crafted, and learning based approaches for underwater single image depth estimation and enhancement.