A Network Combining CNN and Transformer for Blind Image Super-Resolution

Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, th...

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Bibliographic Details
Main Authors: Zhang, Shuhao, Li, Zuoyong, Teng, Shenghua, Zeng, Kun
Format: Conference Proceeding
Language:English
Subjects:
Blind Super-Resolution
CNN
Convolutional neural networks
Education
Estimation
Feature extraction
Kernel
Kernel Estimation
Superresolution
Transformer
Transformers
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Summary:Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining CNN and transformer named NCCT for kernel estimation. By modeling local and non-local image priors simultaneously, NCCT outperforms other blind SR methods in terms of kernel estimation accuracy. Moreover, we design a network module named RRFDB for constructing lightweight blind SR network, which runs faster and achieves comparative SR performance with fewer parameters compared with other state-of-the-art blind SR methods.
ISSN:2474-3828
DOI:10.1109/ITME56794.2022.00091