计算机科学
图像复原
变压器
人工智能
降噪
卷积神经网络
图像去噪
卷积(计算机科学)
计算机视觉
图像(数学)
图像处理
人工神经网络
工程类
电气工程
电压
作者
Chi-Mao Fan,Tsung-Jung Liu,Kuan-Hsien Liu
标识
DOI:10.1109/iscas48785.2022.9937486
摘要
Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost dominated the computer vision and had achieved considerable success in different levels of vision tasks including image restoration. However, recently the Swin Transformer-based model also shows impressive performance, even surpasses the CNN-based methods to become the state-of-the-art on high-level vision tasks. In this paper, we proposed a restoration model called SUNet which uses the Swin Transformer layer as our basic block and then is applied to UNet architecture for image denoising. The source code and pre-trained models are available at https://github.com/FanChiMao/SUNet.
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