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SwinIR: Image Restoration Using Swin Transformer

2021/10/01 by Jingyun Liang, Jiezhang Cao, Guolei Sun +3 · 301 citations
Computer Science · Engineering · #Advanced Image Processing Techniques #Image Processing Techniques and Applications #Image and Signal Denoising Methods

paper · doi:10.1109/iccvw54120.2021.00210

openalex publication_date 2021/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

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). While state-of-the-art 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. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. 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. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced by up to 67%.

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