Single Infrared Super-Resolution via a Shifted Full-Scale Non-Local Network
Image super-resolution technology can reduce the cost and complexity of acquiring high-resolution infrared thermal images. In this study, we propose a novel end-to-end single infrared super-resolution network based on shifted full-scale non-local residual block. A full-scale non-local mechanism is f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10713218/ |
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| Summary: | Image super-resolution technology can reduce the cost and complexity of acquiring high-resolution infrared thermal images. In this study, we propose a novel end-to-end single infrared super-resolution network based on shifted full-scale non-local residual block. A full-scale non-local mechanism is first proposed to enhance the channel and spatial dependencies of the intra-frame feature map. Based on this mechanism, a shifted full-scale non-local residual block is constructed. By confining full-scale non-local to local windows while allowing for shifted-window connectivity, full-scale non-local residual block solves the problem that existed non-local structures are difficult to be reused and serves as backbone for super-resolution network. Qualitative and quantitative evaluation results show that our method has better performance on benchmark datasets of Set5, Set14, BSD100, and the self-built infrared dataset under the up-sample factor of <inline-formula> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\times 3$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula>. |
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| ISSN: | 2169-3536 |