DWTSRNet: Discrete Wavelet Transform Super Resolution Network for medical single image super-resolution

Single image super-resolution generation is a computer vision task that aims to enhance the detail and quality of a low-resolution image, generating a higher-resolution version. To date, several attempts have been made to approach this problem using convolutional neural networks and, more recently, GANs, diffusion models, and transformers. The single-image super-resolution task largely involves the recovery of missing high-frequency details. This has led to the exploration of methods based on the Fourier and wavelet transform. In this work we focused on transformer-based models and wavelets, with the intention of exploiting the advantages of both: the former for its expressive capacity and remarkable achievements in the state of the art, the latter for its ability to decompose a signal into informative components. The proposed model, Discrete Wavelet Transform Super Resolution Network (DWTSRNet), is a transformer-based model composed of three parts: a shallow feature extraction module utilizing the discrete wavelet transform, a deep feature extraction module composed of several residual blocks of SwinV2 Transformer, and a reconstruction module using the inverse discrete wavelet transform. We have conducted experiments on the super-resolution of medical images, specifically focusing on MRI slices of the human brain. The experimental results show that DWTSRNet outperformed other wavelet-based models on standard benchmark datasets and demonstrated promising results on medical datasets. Our code is available at https://github.com/pcestola/DWTSRNet.