Attention-Based Spatial-Frequency Information Network for Underwater Single Image Super-Resolution

We propose a novel deep learning-based (DL) UISR model that incorporates spatial information as well as the transformed (wavelet) coefficient of degraded low-resolution (LR) underwater images by intelligent feature management.

To ensure the visual quality of the super-resolved image, color channel-specific L1 loss, perceptual loss, and difference of Gaussian (DoG) loss are used in tandem with SSIM loss.