Expert Operational GANS: Towards Real-Color Underwater Image Restoration

TL;DR

This paper introduces xOp-GAN, a multi-generator GAN model for underwater image restoration that outperforms single-regressor models by specializing generators for different quality ranges and selecting the best output during inference.

Contribution

The paper proposes xOp-GAN, the first GAN with multiple expert generators and a discriminator used during inference to improve underwater image restoration.

Findings

Achieves PSNR up to 25.16 dB on LSUI dataset

Surpasses all single-regressor models in performance

Reduces complexity compared to existing methods

Abstract

The wide range of deformation artifacts that arise from complex light propagation, scattering, and depth-dependent attenuation makes the underwater image restoration to remain a challenging problem. Like other single deep regressor networks, conventional GAN-based restoration methods struggle to perform well across this heterogeneous domain, since a single generator network is typically insufficient to capture the full range of visual degradations. In order to overcome this limitation, we propose xOp-GAN, a novel GAN model with several expert generator networks, each trained solely on a particular subset with a certain image quality. Thus, each generator can learn to maximize its restoration performance for a particular quality range. Once a xOp-GAN is trained, each generator can restore the input image and the best restored image can then be selected by the discriminator based on its perceptual confidence score. As a result, xOP-GAN is the first GAN model with multiple generators where the discriminator is being used during the inference of the regression task. Experimental results on benchmark Large Scale Underwater Image (LSUI) dataset demonstrates that xOp-GAN achieves PSNR levels up to 25.16 dB, surpassing all single-regressor models by a large margin even, with reduced complexity.