A novel algorithm on adaptive image compressed sensing with sparsity fitting

被引：0

作者：

Wang X.-H. ^{[1
]}

Xu X. ^{[1
]}

Wang W.-J. ^{[1
]}

Gao D.-H. ^{[1
]}

机构：

[1] School of Information and Electronics, Beijing Institute of Technology, Beijing

来源：

Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2017年 / 37卷 / 01期

关键词：

Accurately adaptive sampling; Compressed sensing; Data fitting; Sparsity;

D O I：

10.15918/j.tbit1001-0645.2017.01.018

中图分类号：

学科分类号：

摘要：

When the image is compressed adaptively with compressed sensing theory, the determination of sampling rate and sparsity threshold were highly subjective. In order to solve the problem, an accurately adaptive sampling algorithm with sparsity fitting was proposed in this paper. This algorithm determines the minimum sampling rate under certain sparseness to meet the PSNR requirements by iteration, and an optimal objective function of sparsity-sampling rate choices was obtained with the method of least squares fitting sparsity and sampling rate data. The adaptive sampling algorithm was simulated based on TVAL3.Experimental results show that the PSNR values of reconstructed images are higher than that with the same fixed sampling rate algorithm, and the PSNR difference of clear texture distinction images can reach more than 3.5 dB. Compared to the roughly adaptive algorithm, when the average sampling rate is lower than that, the reconstructed image obtains a higher PSNR value. © 2017, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.

引用

页码：88 / 92

页数：4

共 8 条

[1]

Candes E.J., Compressive sampling, Proceedings of the International Congress of Mathematicians, pp. 1433-1452, (2006)

[2]

Donoho D.L., Compressed sensing, IEEE Transactions on Information Theory, 52, 4, pp. 1289-1306, (2006)

[3]

Haupt J., Nowak R., Compressive sampling vs. conventional imaging, Proceedings of the IEEE International Conference on Image Processing, pp. 1269-1272, (2006)

[4]

Gan L., Block compressed sensing of natural images, Proceedings of the 15th International Conference on Digital Signal Processing, pp. 403-406, (2007)

[5]

Guicquero W., Dupret A., Vandergheynst P., An adaptive compressive sensing with side information, Proceedings of 2013 Asilomar Conference on Signals, Systems and Computers, pp. 138-142, (2013)

[6]

Li Y., Sha X., Wang K., Et al., The weight-block compressed sensing and its application to image reconstruction, Proceedings of 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC), pp. 723-727, (2012)

[7]

Li C., An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing, (2009)

[8]

Stankovic V., Stankovic L., Cheng S., Compressive video sampling, Proceedings of the European Signal Processing Conference, pp. 1433-1452, (2008)

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