Multi-scale adversarial network for underwater image restoration

Abstract

Underwater image restoration, which is the keystone to the underwater vision research, is still a challenging work. The key point of underwater image restoration work is how to remove the turbidity and the color distortion caused by the underwater environment. In this paper, we propose an underwater image restoration method based on transferring an underwater style image into a recovered style using Multi-Scale Cycle Generative Adversarial Network (MCycle GAN) System. We include a Structural Similarity Index Measure loss (SSIM loss), which can provide more flexibility to model the detail structural to improve the image restoration performance. We use dark channel prior (DCP) algorithm to get the transmission map of an image and design an adaptive SSIM loss to improve underwater image quality. We input this information into the network for multi-scale calculation on the images, which achieves the combination of DCP algorithm and Cycle-Consistent Adversarial Networks (CycleGAN). By compared the quantitative and qualitative with existing state-of-the-art approaches, our method shows a pleasing performance on the underwater image dataset.