Multi-Distillation Underwater Image Super-Resolution via Wavelet Transform

By improving image quality and resolution, Single Image Super-Resolution (SISR) models help advance understanding of underwater environments. Super-resolution techniques hold promise in addressing these issues by enhancing image details. The transformer architecture has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). In this paper, we present a novel multi-distillation algorithm based on the swin transformer and wavelet transform (KDSWT) for underwater image super-resolution. A wavelet block is added to the swin transformer to alleviate the loss of detailed features because wavelet transforms can preserve the high-frequency components, subtle variations, and localized features that contribute to the overall richness in images. Based on a large dataset of high-resolution underwater images, a ViT-based student model is trained to learn the intricate features of underwater images from the teacher model. Using knowledge distillation, the student model learns to upscale input images and capture the domain-specific features essential to understanding underwater scenes. To address the computational cost issue in ViT, we introduce a lightweight network training strategy that combines multi-anchor distillation and progressive learning, enabling the lightweight network to achieve exceptional performance and the fastest inference time compared to recent lightweight state-of-art methods. Our experimental results show that KDSWT lightweight (student) significantly improves peak signal-to-noise ratio (PSNR) over the (original) teacher model while decreasing the number of parameters by 54% and the calculation cost by 22%. Concurrently, KDSWT-SR excels beyond state-of-the-art SR techniques on three benchmark underwater datasets, leading to noteworthy improvements in both PSNR and structural similarity index (SSIM).