联合L1和L0先验模型的超分辨率重建算法

doi:10.7523/j.ucas.2020.0013

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (3): 369-376.DOI: 10.7523/j.ucas.2020.0013

• 电子信息与计算机科学 • 上一篇下一篇

联合L₁和L₀先验模型的超分辨率重建算法

李利^1,2,3, 尹增山^1,2,3, 石神^1,2,3

1. 中国科学院微小卫星创新研究院, 上海 201203;
2. 中国科学院大学, 北京 100049;
3. 上海科技大学信息科学与技术学院, 上海 201210

收稿日期:2020-01-23 修回日期:2020-05-05 发布日期:2021-05-31
通讯作者: 尹增山
基金资助:
科技部国家重点研发计划项目(2017YFB0502902)资助

Super-resolution reconstruction algorithm by combining L₁ and L₀ prior models

LI Li^1,2,3, YIN Zengshan^1,2,3, SHI Shen^1,2,3

1. Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201203, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China

Received:2020-01-23 Revised:2020-05-05 Published:2021-05-31

摘要/Abstract

摘要： 超分辨率重建可以从低分辨率图像序列中重建出高分辨率图像,提高图像质量。重建出边缘保持且噪声低的高分辨率图像,仍具有挑战。针对此问题,在L₁先验模型中添加图像梯度的L₀范数作为先验知识,提出联合L₁和L₀先验模型的超分辨率重建算法,既保留L₁先验模型边缘保持的优点,又保留L₀先验模型抑制噪声的优点。将该算法与双三次插值、Total Variation (TV)先验模型和L₁先验模型作对比,通过仿真实验数据和真实实验数据的分析,验证本文算法的有效性。

关键词: 超分辨率重建, L₁先验模型, L₀先验模型, 噪声抑制, 双三次插值

Abstract: Super-resolution (SR) reconstruction can reconstruct a high-resolution image from low-resolution image sequences and improve image quality. Reconstructing a high-resolution image with edge preserving and low noise is still a challenge in SR. Therefore, the L₀ norm of the image gradient is added as prior knowledge in the L₁ prior model, and a SR reconstruction algorithm by combining the L₁ and L₀ prior model is proposed in this paper, which not only retains the advantage of L₁ prior model preserving edges, but also retains the advantage of L₀ prior model suppressing noise. Compared with bicubic interpolation, total variation (TV) prior model, and L₁ prior model, the validity of the algorithm is verified through the analysis of simulation experimental data and real experimental data.

Key words: super-resolution reconstruction, L₁ prior model, L₀ prior model, noise suppression, bicubic interpolation

中图分类号:

TN911.73

李利, 尹增山, 石神. 联合L₁和L₀先验模型的超分辨率重建算法[J]. 中国科学院大学学报, 2022, 39(3): 369-376.

LI Li, YIN Zengshan, SHI Shen. Super-resolution reconstruction algorithm by combining L₁ and L₀ prior models[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(3): 369-376.

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联合L₁和L₀先验模型的超分辨率重建算法

Super-resolution reconstruction algorithm by combining L₁ and L₀ prior models

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