An optimized GAN method based on the Que-Attn and contrastive learning for underwater image enhancement.

ABSTRACT: Research on underwater image processing has increased significantly in the past decade due to the precious resources that exist underwater. However, it is still a challenging problem to restore degraded underwater images. Existing prior-based methods show limited performance in many cases due to their reliance on hand-crafted features. Therefore, in this paper, we propose an effective unsupervised generative adversarial network(GAN) for underwater image restoration. Specifically, we embed the idea of contrastive learning into the model. The method encourages two elements (corresponding patches) to map the similar points in the learned feature space relative to other elements (other patches) in the data set, and maximizes the mutual information between input and output through PatchNCE loss

SUBMITTER: Lan Z 

PROVIDER: S-EPMC9821782 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature