IPMGAN: Integrating physical model and generative adversarial network for underwater image enhancement

Abstract

Autonomous underwater vehicles (AUVs) highly depend on the quality of captured underwater images to perform a variety of tasks. However, compared with everyday images taken in air, underwater images are hazy, with color shift, and in relatively low quality, posing significant challenges to available mature vision algorithms to achieve expected performance. There are, currently, two major lines of approaches to tackle these challenges: the physical image formation model-based and the neural-network-based approaches. In this paper, we propose an integrated approach, where the revised underwater image formation model, i.e., the Akkaynak-Treibitz model, is embedded into the network design for the benefit of combining the advantages of these two approaches. The embedded physical model guides for network learning, and the generative adversarial network (GAN) is adopted for coefficients estimation. We conduct extensive experiments and compare with state-of-the-art approaches quantitatively and qualitatively on nearly all the available underwater datasets, and our method achieves significant improvements.