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Combining Spatial and Frequency Information for Image Deblurring
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AI
DOI：10.1109/LSP.2022.3194807.png)
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摘要
En 中文
This letter aims to combine spatial and frequency information for single image deblurring. Although some methods have tried to use frequency information to perform deblurring, they only simply process the different frequencies information separately or concatenate the real part and imaginary part of frequency features but ignore the strong correlation between them. To address this problem, we propose a simple but effective frequency interaction pipeline to realize the mutual conversion of the real part and the imaginary part. Then, we construct a spatial-frequency conversion module (SFCM) to promote the mutual conversion between the frequency information and the spatial information. Based on the proposed components, we build a multi-scale deblurring network, dubbed SFDNet, which can fully exploit coarse and middle-level information in spatial and frequency domains for finer scale image deblurring. Extensive experiments on the GoPro and HIDE datasets demonstrate that the proposed network outperforms the state-of-the-art methods both quantitatively and visually.
Keyword:
Frequency-domain analysis
Pipelines
Frequency conversion
Image restoration
Training
Kernel
Correlation
Fast Fourier transform
image deblurring
multi-scale network
spatial and frequency conversion
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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