Contrast Analysis of Compressive Sensing OMP and IRLS Algorithm in the Computational Ghost Imaging

作者：DONG Bo; YAO Zhihai; LI Zhe; CHANG Feng; YU Jiayi; WANG Xiaoqian

来源：长春理工大学学报(自然科学版), 2016, (1).

DOI：10.3969/j.issn.1672-9870.2016.01.006

• computational ghost imaging
• compressive sensing
• measurements
• sparsity
• peak signal to noise

The number of measurements can be reduced greatly by combining the compressive sensing technology with ghost imaging system. It can also effectively increase the peak signal to noise ratio of the reconstructed image. In this paper,the discrete cosine transform matrix is used as the image sparse matrix. Two kinds of compressive sensing algo-rithm,the orthogonal matching pursuit algorithm and the iterative weighted least square algorithm,are used as the com-pressed sensing image reconstruction algorithm. We consider the influence of the number of measurements and the spar-sity to peak signal to noise ratio by comparing the variety of peak signal to noise ratio with various values of sparsity and measurements. It is found that the reconstruction accuracy of IRLS algorithm is higher,the image quality is better, and the OMP algorithm is faster than IRLS,and the required time for the reconstruction is less.

• 单位
  长春理工大学