MDNet: A Fusion Generative Adversarial Network for Underwater Image Enhancement

Abstract

Underwater images are widely used in ocean resource exploration and ocean environment surveillance. However, due to the influence of light attenuation and noise, underwater images usually display degradation phenomena such as blurring and color deviation; an enhancement method is required to make the images more visible. Currently, there are two major approaches for image enhancement: the traditional methods based on physical or non-physical models, and the deep learning method. Inspired by the fusion-based idea, this paper attempts to combine traditional methods with deep learning and proposes a multi-input dense connection generator network (MDNet) for underwater image enhancement. Raw images and processed images are input into the network together, the shallow information is fully utilized by dense connection, and the network is trained in generative and adversarial manner. We also design a multiple loss function to improve the visual quality of the generated images. We conduct both qualitative and quantitative experiments, and then compare the results with state-of-the-art approaches comprehensively using three representative datasets. Results show that the proposed method can effectively improve the perceptual and statistical quality of underwater images.

Keywords

• underwater image enhancement
• deep learning
• generative adversarial network