SwinIR: Image Restoration Using Swin Transformer

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction.

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• 1 While advanced image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks.
• 2 In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer.
• 3 SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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