量子电子学报

• 图像与信息处理 • 上一篇    下一篇

不同压缩感知重建算法在鬼成像中的性能比较

王梦涵,张兆奇,赵生妹   

  1. 南京邮电大学信号处理与传输研究院,江苏 南京,210003
  • 出版日期:2017-09-28 发布日期:2019-06-13
  • 基金资助:
    Supported by National Natural Science Foundation of China(国家自然科学基金, 61271238,61475075)

Performance comparison of different compressed sensing reconstruction algorithms in ghost imaging

WANG Menghan ZHANG Zhaoqi ZHAO Shengmei   

  1. Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications,Nanjing 210003, China
  • Published:2017-09-28 Online:2019-06-13

摘要: 基于鬼成像(GI)与压缩感知(CS)理论,研究了CS重建算法对GI成像性能的影响。以离散小波变换为图像的稀疏矩阵、具有高斯线型的热光源强度分布为测量矩阵,分析了基于增广拉格朗日法和交替方向法的全变分最小化算法(TVAL3)、正交匹配追踪算法(OMP)、压缩采样匹配追踪算法(CoSaMP)、梯度投影算法(GPSR_Basic)下的压缩鬼成像的质量。以均方误差、峰值信噪比、匹配度、结构相似性指标等为图像质量客观评价标准,比较了4种重建算法下压缩鬼成像的重建结果。结果表明压缩比为0.5时TVAL3算法还原度最高,CoSaMP算法重建图像失真最严重,GPSR_Basic算法获得的重建性能优于OMP算法。

关键词: 图像处理, 重建算法, 压缩感知, 鬼成像

Abstract: Based on ghost imaging(GI) and compressed sensing(CS) theory, the influence of CS reconstruction algorithm on imaging performance of GI is investigated. Discrete wavelet transform is used as image sparse matrix, and the thermal light source intensity distribution with Gauss profile is used as measurement matrix. Quality of compressed GI is analyzed with total variation minimization algorithm based on augmented Lagrangian and alternating direction method (TVAL3), orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP) algorithm, gradient projection algorithm (GPSR basic). Mean square error (MSE), peak signal to noise ratio (PSNR), matching degree (MR) and structural similarity index (SSIM) are taken as the objective evaluation criteria for image quality, and the reconstruction results of GI using four kinds of reconstruction algorithms are compared. Results show that when the compression ratio is 0.5, the reduction degree of TVAL3 algorithm is the highest, and the distortion of CoSaMP algorithm is the most serious. The reconstruction performance of GPSR_Basic algorithm is better than that of OMP algorithm.

Key words: image processing, reconstruction algorithm, compressed sensing, ghost imaging