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

Alik Pramanick, Dhruvil Megha, Arijit Sur

Abstract: Underwater single image super-resolution (UISR) is a challenging task as these images frequently suffer from poor visibility. The best-published UISR works continue to suffer from color degradation, poor texture representation, and loss of finer (high-frequency) details. 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. We employ publicly available datasets, namely UFO-120 and USR-248, to evaluate the proposed model. The results of our experiments show that our model outperforms existing state-of-the-art methods (e.g., $\sim 9.45\% $/$\sim 1.77\% $ in SSIM and $\sim 0.91\% $/ $\sim 1.44\% $ in PSNR on UFO-120/USR-248 ×4, respectively), as demonstrated through quantitative measurements and visual quality assessments.