Boosting Flow-based Generative Super-Resolution Models via Learned Prior
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911891548274688 |
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| author | Tsao, Li-Yuan Lo, Yi-Chen Chang, Chia-Che Chen, Hao-Wei Tseng, Roy Feng, Chien Lee, Chun-Yi |
| author_facet | Tsao, Li-Yuan Lo, Yi-Chen Chang, Chia-Che Chen, Hao-Wei Tseng, Roy Feng, Chien Lee, Chun-Yi |
| contents | Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and suboptimal results due to a fixed sampling temperature. To overcome these issues, this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: https://github.com/liyuantsao/BFSR |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10988 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting Flow-based Generative Super-Resolution Models via Learned Prior Tsao, Li-Yuan Lo, Yi-Chen Chang, Chia-Che Chen, Hao-Wei Tseng, Roy Feng, Chien Lee, Chun-Yi Computer Vision and Pattern Recognition Artificial Intelligence Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and suboptimal results due to a fixed sampling temperature. To overcome these issues, this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: https://github.com/liyuantsao/BFSR |
| title | Boosting Flow-based Generative Super-Resolution Models via Learned Prior |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.10988 |