Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909532353986560 |
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| author | Peng, Long Wu, Anran Li, Wenbo Xia, Peizhe Dai, Xueyuan Zhang, Xinjie Di, Xin Sun, Haoze Pei, Renjing Wang, Yang Cao, Yang Zha, Zheng-Jun |
| author_facet | Peng, Long Wu, Anran Li, Wenbo Xia, Peizhe Dai, Xueyuan Zhang, Xinjie Di, Xin Sun, Haoze Pei, Renjing Wang, Yang Cao, Yang Zha, Zheng-Jun |
| contents | Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addressing the limitations of traditional SR methods constrained to fixed-scale factors (\textit{e.g.}, $\times$ 2). Recent advances leveraging implicit neural representation (INR) have achieved great progress by modeling coordinate-to-pixel mappings. However, the efficiency of these methods may suffer from repeated upsampling and decoding, while their reconstruction fidelity and quality are constrained by the intrinsic representational limitations of coordinate-based functions. To address these challenges, we propose a novel ContinuousSR framework with a Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting. This approach eliminates the need for time-consuming upsampling and decoding, enabling extremely fast arbitrary-scale super-resolution. Once the Gaussian field is built in a single pass, ContinuousSR can perform arbitrary-scale rendering in just 1ms per scale. Our method introduces several key innovations. Through statistical ana |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06617 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling Peng, Long Wu, Anran Li, Wenbo Xia, Peizhe Dai, Xueyuan Zhang, Xinjie Di, Xin Sun, Haoze Pei, Renjing Wang, Yang Cao, Yang Zha, Zheng-Jun Computer Vision and Pattern Recognition Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addressing the limitations of traditional SR methods constrained to fixed-scale factors (\textit{e.g.}, $\times$ 2). Recent advances leveraging implicit neural representation (INR) have achieved great progress by modeling coordinate-to-pixel mappings. However, the efficiency of these methods may suffer from repeated upsampling and decoding, while their reconstruction fidelity and quality are constrained by the intrinsic representational limitations of coordinate-based functions. To address these challenges, we propose a novel ContinuousSR framework with a Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting. This approach eliminates the need for time-consuming upsampling and decoding, enabling extremely fast arbitrary-scale super-resolution. Once the Gaussian field is built in a single pass, ContinuousSR can perform arbitrary-scale rendering in just 1ms per scale. Our method introduces several key innovations. Through statistical ana |
| title | Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.06617 |