Depth-Conditioned GAN for Underwater Image Enhancement

Autonomous underwater vehicles perceive environmental information relying on vision. However, the variety of the underwater environment always leads to image distortion. To effectively obtain color-corrected images that guarantee high sharpness and good content recovery, this paper proposes an algorithm based on deep learning networks. Our method is a modified version of generative adversarial networks for underwater image enhancement. Due to the complexity of the underwater environment, the green hue in the image is uneven, and the existing methods cannot recover the uneven green hue image well. Using our model, we generate corresponding depth estimation maps, along with the real underwater images, as inputs to the generator for color correction of monocular underwater images. The depth estimation maps, which are considered as image noise in the input, helps to distinguish the region of interest from the background. Furthermore, we formulate an objective function through several different loss functions, which can evaluate image quality globally. In addition, we performed training on the EUVP dataset, the comparison results show that our method is superior to other methods. Finally, we conduct extensive experiments on EUVP, UIEB and UFO-120 datasets, where our model not only significantly improves the quality of the underwater images, but also performs well over the state-of-the-art methods.