Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling

Long Peng; Anran Wu; Wenbo Li; Peizhe Xia; Xueyuan Dai; Xinjie Zhang; Xin Di; Haoze Sun; Renjing Pei; Yang Wang; Yang Cao; Zheng-Jun Zha

arXiv · 2503.06617

Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling

Long Peng·Anran Wu·Wenbo Li·Peizhe Xia·Xueyuan Dai·Xinjie Zhang·Xin Di·Haoze Sun·Renjing Pei·Yang Wang·Yang Cao·Zheng-Jun Zha

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Abstract

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

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BibTeX RIS

Long Peng, Anran Wu, Wenbo Li, Peizhe Xia, Xueyuan Dai, Xinjie Zhang, Xin Di, Haoze Sun, Renjing Pei, Yang Wang, Yang Cao, Zheng-Jun Zha. 2025-03-09. Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling. https://arxiv.org/abs/2503.06617

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