SwinIR: Image Restoration Using Swin Transformer
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2021-10
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Conference Paper
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yes
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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 similar to 0.45dB, while the total number of parameters can be reduced by up to 67%.
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2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2021)
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1833 - 1844
Publisher
IEEE
Event
Advances in Image Manipulation Workshop (AIM 2021) in conjunction with ICCV 2021
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03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)