Agcyclegan: Attention-Guided Cyclegan for Single Underwater Image Restoration

Abstract

Underwater image restoration is a fundamental problem in image processing and computer vision. It has broad application prospects for underwater operations, especially underwater robot operations. The challenging work is how to keep the color authenticity of the captured underwater image. In this paper, we propose a novel network architecture based on CycleGAN. Specifically, in the generator part, we adopt the U-Net structure because the long skip connection of U-Net will obtain more detailed information. Besides, we append the pixel-level attention block to provide greater flexibility for detail structure modeling. It assigns different weights to each channel to pay more attention to the critical feature. We also verify its generalization performance on several benchmark datasets. The extensive experiments with comparisons to state-of-the-art approaches demonstrate the superiority of the proposed model.