Title: Towards Quality Advancement of Underwater Machine Vision with Generative Adversarial Networks,
Abstract: Underwater machine vision has attracted significant attention, but its low
quality has prevented it from a wide range of applications. Although many
different algorithms have been developed to solve this problem, real-time
adaptive methods are frequently deficient. In this paper, based on filtering
and the use of generative adversarial networks (GANs), two approaches are
proposed for the aforementioned issue, i.e., a filtering-based restoration
scheme (FRS) and a GAN-based restoration scheme (GAN-RS). Distinct from
previous methods, FRS restores underwater images in the Fourier domain, which
is composed of a parameter search, filtering, and enhancement. Aiming to
further improve the image quality, GAN-RS can adaptively restore underwater
machine vision in real time without the need for pretreatment. In particular,
information in the Lab color space and the dark channel is developed as loss
functions, namely, underwater index loss and dark channel prior loss,
respectively. More specifically, learning from the underwater index, the
discriminator is equipped with a carefully crafted underwater branch to predict
the underwater probability of an image. A multi-stage loss strategy is then
developed to guarantee the effective training of GANs. Through extensive
comparisons on the image quality and applications, the superiority of the
proposed approaches is confirmed. Consequently, the GAN-RS is considerably
faster and achieves a state-of-the-art performance in terms of the color
correction, contrast stretch, dehazing, and feature restoration of various
underwater scenes. The source code will be made available.