Local-CycleGAN: a general end-to-end network for visual enhancement in complex deep-water environment

Abstract

Underwater image analysis is crucial for many applications such as seafloor survey, biological and environment monitoring, underwater vehicle navigation, inspection and maintenance of underwater infrastructure etc. However, due to light absorption and scattering, the images acquired underwater are always blurry and distorted in color. Most existing image enhancement algorithms typically focus on a few features of the imaging environments, and enhanced results depend on the characteristics of original images. In this study, a local cycle-consistent generative adversarial network is proposed to enhance images acquired in a complex deep-water environment. The proposed network uses a combination of a local discriminator and a global discriminator. Additionally, quality-monitor loss is adopted to evaluate the effect of the generated images. Experimental results show that the local cycle-consistent generative adversarial network is robust and can be generalized for many different image enhancement tasks in different types of complex deep-water environment with varied turbidity.