Fast underwater image enhancement based on a generative adversarial framework

Abstract

Underwater image enhancement is a fundamental requirement in the field of underwater vision. Along with the development of deep learning, underwater image enhancement has made remarkable progress. However, most deep learning-based enhancement methods are computationally expensive, restricting their application in real-time large-size underwater image processing. Furthermore, GAN-based methods tend to generate spatially inconsistent styles that decrease the enhanced image quality. We propose a novel efficiency model, FSpiral-GAN, based on a generative adversarial framework for large-size underwater image enhancement to solve these problems. We design our model with equal upsampling blocks (EUBs), equal downsampling blocks (EDBs) and lightweight residual channel attention blocks (RCABs), effectively simplifying the network structure and solving the spatial inconsistency problem. Enhancement experiments on many real underwater datasets demonstrate our model's advanced performance and improved efficiency.