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

Arxiv 2025


Long Peng1,3†, Anran Wu1,2†, Wenbo Li3*, Peizhe Xia1, Xueyuan Dai4,
Xinjie Zhang5, Haoze Sun6, Yang Wang1,4*, Yang Cao1, Zheng-Jun Zha1,

1USTC, 2AHU, 3Huawei Noah’s Ark Lab, 4Chang’an University, 5HKUST, 6THU

arXiv Code

Motivations

Compared to other methods, the proposed ContinuousSR delivers significant improvements in SR quality across all scales, with an impressive 19.5× speedup when continuously upsampling an image across forty scales.

Contributions

  1. A novel ContinuousSR is proposed to reconstruct continuous HR signals from LR images by 2D Gaussian modeling, thereby enabling fast and high-quality super-resolution with arbitrary scale.
  2. The Deep Gaussian Prior (DGP) is discovered, based on which DGP-Driven Covariance Weighting is proposed to facilitate the optimization of covariance. Furthermore, Adaptive Position Drifting is introduced to dynamically learn spatial positions in Gaussian space.
  3. Extensive experiments demonstrate that our method achieves state-of-the-art performance on seven benchmarks and ultra-fast speed.

Framework

(a) Directly learning the end-to-end model from LR to the Gaussian field is challenging due to the vastness and sensitivity of the Gaussian space. (b-c) Through statistical analysis of 40,000 natural images, we uncover the Deep Gaussian Prior and propose Position Drifting, Covariance Prior, and Color Mapping to propose a novel ContinuousSR, enhancing the quality of the Gaussian field.

ContinuousSR

An overview of the proposed ContinuousSR framework, which consists of three key innovations: DGP-Driven Covariance Weighting (DDCW), Adaptive Position Drifting (APD), and Color Gaussian Mapping (CGM).

Performance

PSNR performance comparison with state-of-the-art methods under different benchmarks. Average Time (AT) is reported in milliseconds (ms)

Visual Results

Qualitative comparison. The visual quality of our method outperforms existing methods. Please zoom in for a better view.

Citation

If you are interested in the following work, please cite the following paper. 

      @article{peng2025pixel,
        title={Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling},
        author={Peng, Long and Wu, Anran and Li, Wenbo and Xia, Peizhe and Dai, Xueyuan and Zhang, Xinjie and Di, Xin and Sun, Haoze and Pei, Renjing and Wang, Yang and others},
        journal={arXiv preprint arXiv:2503.06617},
        year={2025}
    }