计算机与现代化 ›› 2020, Vol. 0 ›› Issue (12): 104-111.

• 图像处理 • 上一篇    下一篇

基于SWOMP算法的EPMA影像优化重构

  

  1. (1.东华理工大学信息工程学院,江西南昌330013;2.东华理工大学江西省核地学数据科学与系统工程技术
    研究中心,江西南昌330013;3.东华理工大学软件学院,江西南昌330013)
  • 出版日期:2021-01-07 发布日期:2021-01-07
  • 作者简介:金安安(1995—),男,江西南昌人,硕士研究生,研究方向:图像处理,E-mail: anan_1995@126.com; 丁双双(1994—),女,山东菏泽人,硕士研究生,研究方向:嵌入式应用,E-mail: 2425630640@qq.com; 熊卿智(1998—),男,江西南昌人,本科生,研究方向:计算机应用,E-mail: 2919854239@qq.com; 许婷婷(1996—),女,江西九江人,硕士研究生,研究方向:应用程序通信安全机制,E-mail: 1585513771@qq.com; 习淑萍(1997—),女,江西吉安人,硕士研究生,研究方向:图形图像处理,E-mail: 1639447959@qq.com; 马继忠(1996—),男,安徽阜阳人,硕士研究生,研究方向:嵌入式开发应用,E-mail: 1312366250@qq.com。
  • 基金资助:
    江西省核地学数据科学与系统工程技术研究中心开发基金资助项目(JETRCNGDSS201801)

Optimization Reconstruction of EPMA Image Based on SWOMP Algorithm

  1. (1. School of Information Engineering, East China University of Technology, Nanchang 330013, China;2. Jiangxi Engineering 
    Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China;
    3. School of Software, East China University of Technology, Nanchang 330013, China)
  • Online:2021-01-07 Published:2021-01-07

摘要: 压缩感知历经多年发展,重构算法也比较多,其中分段弱正交匹配追踪(SWOMP)算法是一种改进算法,该算法对稀疏度没有要求,测量矩阵选择高斯矩阵,但是其重构效果并不理想。针对该算法的不足,同时结合电子探针影像,对该算法进行优化。该优化充分利用傅里叶矩阵的优势,同时对迭代次数和门限参数进行调整。首先,对常用的矩阵进行多次试验,找出最优质的测量矩阵——傅里叶正交矩阵;其次,对迭代次数和阈值进行修改,寻找最佳参数搭配,提高该算法重构质量。实验结果表明,本文方法在电子探针图像上的重构效果较好,达到超分辨率恢复要求,所重构的图像质量高于原有算法。

关键词: 图像处理, 压缩感知, 电子探针, 超分辨率重建, 正交匹配追踪

Abstract: Compressed sensing has been developed for many years, and there are many reconstruction algorithms. The stagewise weak orthogonal matching pursuit (SWOMP) algorithm, which does not require sparsity, is an improved algorithm. The measurement matrix selects Gaussian matrix, but its reconstruction effect is not ideal. Aiming at the shortcomings of the algorithm, the algorithm is optimized by combining the electron probe image. This optimization takes full advantage of the Fourier matrix and adjusts the number of iterations and threshold parameters. Firstly, the commonly used matrix is tested several times to find the best quality measurement matrix—Fourier orthogonal matrix. Secondly, the iteration number and threshold are modified to find the best parameter matching to improve the reconstruction quality of the algorithm. The experimental results show that the proposed method has better reconstruction effect on the electron probe image and achieves the super-resolution recovery requirement. The reconstructed image quality is higher than the original one.

Key words: image processing, compressed sensing, electron probe, super-resolution reconstruction, orthogonal matching pursuit