Improved optimization algorithm for measurement matrix in compressed sensing

被引：1
作者：
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing [1 ]
210016, China
不详 [2 ]
210016, China
机构：
[1] College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] College of Electronic and Information Engineering, Nanjing
来源：
Xi Tong Cheng Yu Dian Zi Ji Shu/Syst Eng Electron | / 4卷 / 752-756期
关键词：
Compressed sensing (CS); Gradient descent; Measurement matrix; Optimization; Varied step;
D O I：
10.3969/j.issn.1001-506X.2015.04.05
中图分类号：
学科分类号：
摘要：
The signal recovery performance of compressed sensing (CS) requires that the cross correlations between the measurement matrix and sparse transformed matrix should be as small as possible. In order to reduce the cross correlations, an varied step gradient descent algorithm is studied and si-mulated annealing (SA) learning rate factor is introduced to adjust the step function. The simulation results demonstrate that due to the adaptive adjustment of step length in the iteration process, the speed of optimizing matrix is fast, more mutual coherence coefficients are distributed around zero, and the peak signal to noise ratio of reconstructed image is improved with the optimized measurement matrix. The improved algorithm has good performance in achieving lower mutual coherence and improving reconstruction performance. ©, 2015, Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：752 / 756
页数：4
相关论文
共 20 条
  • [1] Donoho D., Compressed sensing, IEEE Trans. on Information Theory, 52, 4, pp. 1289-1306, (2006)
  • [2] Candes E., Romberg J., Tao T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. on Information Theory, 52, 2, pp. 489-509, (2006)
  • [3] Elad M., Michal A., Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. on Image Processing, 15, 12, pp. 3736-3745, (2006)
  • [4] Chang H., Lu G.Y., Feng J.Y., Unambiguous wideband DOA estimation based on compressed sensing, Systems Engineering and Electronics, 35, 5, pp. 920-923, (2013)
  • [5] Zhou T.Y., Tao D.C., 1-bit hamming compressed sensing, Proc. of the IEEE International Symposium Information Theory, pp. 1862-1866, (2012)
  • [6] Ling X.X., Wei Z.H., Xiao L., Et al., Compressive sensing image reconctruction algorithm based on non-local regularization, Systems Engineering and Electronics, 35, 1, pp. 196-202, (2013)
  • [7] Candes E., The restricted isometry property and its implications for compressed sensing, Comptes Rendus Mathematique, 346, 9-10, pp. 589-592, (2008)
  • [8] Baraniuk R., A lecture on compressive sensing, IEEE Signal Processing Magazine, 24, 4, pp. 118-121, (2007)
  • [9] Nhat V.D.M., Vo D., Challa S., Et al., Efficient projection for compressed sensing, Proc. of the Computer and Information Science, pp. 322-327, (2008)
  • [10] Elad M., Optimized projections for compressed sensing, IEEE Trans. on Signal Processing, 55, 12, pp. 5695-5702, (2007)
12