数据保真项与稀疏约束项相融合的稀疏重建

高红霞; 谢剑河; 曾润浩; 吴梓灵; 马鸽

doi:10.3788/OPE.20172509.2437

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数据保真项与稀疏约束项相融合的稀疏重建

信息科学 | 更新时间：2020-08-13

- 数据保真项与稀疏约束项相融合的稀疏重建
- Sparse reconstruction method based on integrating data fidelity term and sparse constraint term
- 光学精密工程 2017年25卷第9期页码：2437-2447
- 作者机构：
  
  1.华南理工大学自动化科学与工程学院, 广东广州 510640
  2.华南理工大学精密电子制造装备教育部研究中心, 广东广州 510640
  3.广州大学机械与电气工程学院, 广东广州 510006
- 作者简介：
  
  [ "高红霞(1975-), 女, 陕西富平人, 博士, 教授, 博士生导师, 1996年、2000年于西安交通大学分别获得学士、硕士学位, 2003年于中国科学院自动化研究所获得博士学位, 主要从事复杂成像条件下图像的复原和重建研究.E-mail:hxgao@scut.edu.cn" ]
  [ "谢剑河(1990-), 男, 广东潮州人, 硕士, 2013年、2016年于华南理工大学分别获得学士、硕士学位, 主要从事机器视觉、图像处理方面的研究.E-mail:xiejianhe_2006@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61403146);广州市科技计划项目(201707010054);中央高校基本科研业务费资助项目(2015ZM128)
- DOI：10.3788/OPE.20172509.2437
  中图分类号： TP394.1;TP242.6
- 收稿日期：2017-01-03，
  
  录用日期：2017-3-13，
  
  纸质出版日期：2017-09-25
- 稿件说明：
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高红霞, 谢剑河, 曾润浩, 等. 数据保真项与稀疏约束项相融合的稀疏重建[J]. 光学精密工程, 2017,25(9):2437-2447.

Hong-xia GAO, Jian-he XIE, Run-hao ZENG, et al. Sparse reconstruction method based on integrating data fidelity term and sparse constraint term[J]. Optics and precision engineering, 2017, 25(9): 2437-2447.
高红霞, 谢剑河, 曾润浩, 等. 数据保真项与稀疏约束项相融合的稀疏重建[J]. 光学精密工程, 2017,25(9):2437-2447. DOI： 10.3788/OPE.20172509.2437.

Hong-xia GAO, Jian-he XIE, Run-hao ZENG, et al. Sparse reconstruction method based on integrating data fidelity term and sparse constraint term[J]. Optics and precision engineering, 2017, 25(9): 2437-2447. DOI： 10.3788/OPE.20172509.2437.

摘要

本文针对低光子计数成像过程中产生的泊松高斯混合噪声，提出了一种数据保真项与稀疏约束项相融合的稀疏重建方法。首先，基于泊松高斯噪声相互独立的混合噪声模型，建立了数据保真项与稀疏约束项相融合的稀疏重建目标函数；在图像块聚类的基础上，应用改进贪婪算法实现类内稀疏分解和字典更新；最后，稀疏分解和字典更新交替迭代求解干净图像。针对强烈泊松高斯噪声污染图像的重建实验显示，本文方法与对比方法相比，重建结果的PSNR值平均提升了5.5%，MSSIM值也有明显提升。这些结果表明：本文方法对具有强烈泊松高斯混合噪声的图像有较好的图像复原和噪声去除效果。

Abstract

Aiming at the process of low-dose photon counting imaging with Poisson-Gaussian mixed noise

a sparse reconstruction method of integrating data fidelity term and sparse constrait term is proposed. Firstly

based on the hypothesis that Poisson and Gaussian noise are mutually independent

the sparse reconstructing objective function based on integrating data fidelity term and sparsity constraint term is established. Based on patch clustering

the improved greedy algorithm is applied to implement sparse decomposition and dictionary update. Finally

a clean image is obtained by alternating iteration. Contrast experiments on images corrupted with strong Poisson-Gaussian mixed noise show that the average PSNR of image reconstructed by the proposed method increased by 5.5% more than those of the contrast methods

moreover

their MSSIM increased significantly. The experiment results demonstrate that the proposed method has better image restoration and denoising effect for low photon counting image with strong Poisson-Gaussian mixed noise.

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Keywords

references

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