AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena

Abstract

ABSTRACT This study proposes an innovative deep learning-aided approach based on generative adversarial networks named AttGAN, which is specialized for solving the spatio-temporal super-resolution problem of ocean datasets with complex waveforms and turbulent features. The proposed method can efficiently restore the desired fine high-frequency details while maintaining high efficiency. The key idea of the proposed approach is to incorporate an attention gate in the generator, which allows for emphasizing salient features for super-resolution tasks. In addition, residual convolutional blocks are used in the generator and discriminator to extract features. By establishing regression loss, physical constraints, and adversarial loss into a comprehensive loss function, a generator that exhibits high fidelity and strong generalization capabilities is trained. The proposed AttGAN is evaluated by experiments conducted on two datasets, and the experimental results validate its superiority over state-of-the-art physical constraint models in both qualitative and quantitative metrics. Moreover, the AttGAN has faster processing times than the corresponding ocean numerical models and better quality of results. Impact statement