Overview
SwinIR is a deep learning model based on the Swin Transformer architecture, designed for image restoration tasks. It addresses common image quality issues such as low resolution, noise, and compression artifacts. The model comprises three main components: shallow feature extraction, deep feature extraction using residual Swin Transformer blocks (RSTB), and high-quality image reconstruction. Each RSTB consists of multiple Swin Transformer layers with residual connections, enabling efficient processing of image features. SwinIR supports image super-resolution (classical, lightweight, real-world), image denoising (grayscale and color), and JPEG compression artifact reduction. The official PyTorch implementation is available on GitHub, along with pretrained models and visual results. It is noted for achieving state-of-the-art performance while reducing the number of parameters compared to convolutional neural networks.