Efficient Cost-and-Quality Controllable Arbitrary-scale Super-resolution with Fourier Constraints
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914117783126016 |
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| author | Akita, Kazutoshi Ukita, Norimichi |
| author_facet | Akita, Kazutoshi Ukita, Norimichi |
| contents | Cost-and-Quality (CQ) controllability in arbitrary-scale super-resolution is crucial. Existing methods predict Fourier components one by one using a recurrent neural network. However, this approach leads to performance degradation and inefficiency due to independent prediction. This paper proposes predicting multiple components jointly to improve both quality and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23978 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Cost-and-Quality Controllable Arbitrary-scale Super-resolution with Fourier Constraints Akita, Kazutoshi Ukita, Norimichi Computer Vision and Pattern Recognition Cost-and-Quality (CQ) controllability in arbitrary-scale super-resolution is crucial. Existing methods predict Fourier components one by one using a recurrent neural network. However, this approach leads to performance degradation and inefficiency due to independent prediction. This paper proposes predicting multiple components jointly to improve both quality and efficiency. |
| title | Efficient Cost-and-Quality Controllable Arbitrary-scale Super-resolution with Fourier Constraints |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.23978 |