A transformer-based network for perceptual contrastive underwater image enhancement

Abstract

Vision-based underwater image enhancement methods have received much attention for application in the fields of marine engineering and marine science. The absorption and scattering of light in real underwater scenes leads to severe information degradation in the acquired underwater images, thus limiting further development of underwater tasks. To solve these problems, a novel transformer-based perceptual contrastive network for underwater image enhancement methods (TPC-UIE) is proposed to achieve visually friendly and high-quality images, where contrastive learning is applied to the underwater image enhancement (UIE) task for the first time. Specifically, to address the limitations of the pure convolution-based network, we embed the transformer into the UIE network to improve its ability to capture global dependencies. Then, the limits of the transformer are then taken into account as convolution is reintroduced to better capture local attention. At the same time, the dual-attention module strengthens the network’s focus on the spatial and color channels that are more severely attenuated. Finally, a perceptual contrastive regularization method is proposed, where a multi-loss function made up of reconstruction loss, perceptual loss, and contrastive loss jointly optimizes the model to simultaneously ensure texture detail, contrast, and color consistency. Experimental results on several existing datasets show that the TPC-UIE obtains excellent performance in both subjective and objective evaluations compared to other methods. In addition, the visual quality of the underwater images is significantly improved by the enhancement of the method and effectively facilitates further development of the underwater task.