Threshold multipath sparsity adaptive image reconstruction algorithm based on compressed sensing

被引：0
|
作者：
Zhu S. [1 ]
Zhang L. [1 ]
Ning J. [1 ]
Jin M. [1 ]
机构：
[1] School of Information and Communication Engineering, Dalian University of Technology, Dalian
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 10期
关键词：
Compressed sensing (CS); Image reconsturction; Sparsity adaptive; Threshold multipath;
D O I：
10.3969/j.issn.1001-506X.2019.10.06
中图分类号：
学科分类号：
摘要：
Aiming at the problem that depth-first multipath matching pursuit algorithm needs known image sparsity and high computational complexity in image reconstruction, a threshold multipath sparsity adaptive image reconstruction algorithm is proposed. In this algorithm, multiple candidate sets are introduced, and thresholds are set to select atoms and adjust the number of candidate sets. Then each iteration selects the path with the smallest residual as a new candidate set to improve the reconstruction speed. In addition, residual difference less than a threshold is used as the stopping condition of the algorithm, so image sparsity is not needed as the input of the algorithm. The experimental results show that the algorithm can achieve good reconstruction effect, while maintaining good time complexity and anti-noise performance. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：2191 / 2197
页数：6
相关论文
共 26 条
  • [1] Donoho D., Compressed sensing, IEEE Trans.on Information Theory, 52, 4, pp. 1289-1306, (2006)
  • [2] Candes E., Wakin M., An introduction to compressive sampling, IEEE Signal Processing Magazine, 25, 2, pp. 21-30, (2008)
  • [3] Candes E.J., Romberg J., Tao T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans.on Information Therory, 52, 2, pp. 489-509, (2006)
  • [4] Chen S.S., Donoho D.L., Saunders M.A., Atomic decompo-sition by basis pursuit, SIAM Review, 43, 1, pp. 129-159, (2001)
  • [5] Meszaros C., Regularization techniques in interior point methods, Journal of Computational and Applied Mathematics, 236, 15, pp. 3704-3709, (2012)
  • [6] Blumensath T., Davies M.E., Iterative hard thresholding for compressed sensing, Applied and Computational Harmonic Analysis, 27, 3, pp. 265-274, (2009)
  • [7] Figueiredo M.A.T., Nowak R.D., Wright S.J., Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems, IEEE Journal of Selected Topics in Signal Processing, 1, 4, pp. 586-597, (2007)
  • [8] Mousavi A., Patel A.B., Baraniuk R.G., A deep learning approach to structured signal recovery, Proc.of the 53rd Annual Allerton Conference on Communication, Control and Computing, pp. 1336-1343, (2015)
  • [9] Adler A., Boublil D., Zibulevsky M., Block-based compressed sensing of images via deep learning, Proc.of the 19th IEEE International Workshop on Multimedia Signal Processing, pp. 1-6, (2017)
  • [10] Jin K.H., Mccann M.T., Froustey E., Et al., Deep convolutional neural network for inverse problems in imaging, IEEE Trans.on Image Processing, 26, 9, pp. 4509-4522, (2017)
123