A vision transformer based CNN for underwater image enhancement ViTClarityNet

Abstract

Underwater computer vision faces significant challenges from light scattering, absorption, and poor illumination, which severely impact underwater vision tasks. To address these issues, ViT-Clarity, an underwater image enhancement module, is introduced, which integrates vision transformers with a convolutional neural network for superior performance. For comparison, ClarityNet, a transformer-free variant of the architecture, is presented to highlight the transformer's impact. Given the limited availability of paired underwater image datasets (clear and degraded), BlueStyleGAN is proposed as a generative model to create synthetic underwater images from clear in-air images by simulating realistic attenuation effects. BlueStyleGAN is evaluated against existing state-of-the-art synthetic dataset generators in terms of training stability and realism. Vit-ClarityNet is rigorously tested on five datasets representing diverse underwater conditions and compared with recent state-of-the-art methods as well as ClarityNet. Evaluations include qualitative and quantitative metrics such as UCIQM, UCIQE, and the deep learning-based URanker. Additionally, the impact of enhanced images on object detection and SIFT feature matching is assessed, demonstrating the practical benefits of image enhancement for underwater computer vision tasks.