Enhancing Image Restoration Performance with Hierarchical Swin Transformers

Image restoration is a critical task in computer vision, addressing challenges such as image denoising, super-resolution, and compression artifact removal. While convolutional neural networks (CNNs) have traditionally been the backbone of image restoration techniques, they often struggle to model long-range dependencies due to their inherently local receptive fields. In recent years, transformer-based architectures have gained attention for their ability to capture global context effectively. This paper presents a comprehensive study of SwinIR, an advanced image restoration framework based on the Swin Transformer. SwinIR introduces shifted window mechanisms and a hierarchical architecture, allowing efficient modeling of both local and long-range image features. We explore how these innovations contribute to improved restoration performance and better generalization across diverse image degradation scenarios. Extensive experiments are conducted on standard benchmark datasets, covering a wide range of restoration tasks. Our results show that SwinIR consistently surpasses state-of-the-art CNN-based methods in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality. The findings suggest that Swin Transformer-based models offer a powerful alternative to traditional approaches, paving the way for more accurate and scalable image restoration solutions.