Rapid Gradient Projection Algorithm for Image Reconstruction

Hai Xia Yan; Yan Jun Liu

doi:10.4028/www.scientific.net/AMM.530-531.443

Paper Titles

Joint Multiple Visual Constraints for High Speed Video Stitching
p.418

PCA-ANFIS Based Prediction for Water Injection Effectiveness Cycle in Oil Fields
p.422

Power Information System Risk Assessment Method Based on Genetic Algorithms and Neural Network
p.429

Randomness Evaluation for Scrambled Image Using Intersecting Cortical Model Neural Network
p.434

Rapid Gradient Projection Algorithm for Image Reconstruction
p.443

Research on Intelligent Knowledge Recommendation System for Police Applications
p.447

Simulations of Linearity of Buffon’s Needle Problem
p.452

Steel Strip Defect Detection Based on Human Visual Attention Mechanism Model
p.456

Study on Data Fusion and Optimization Based on Neural Networks
p.463

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 530-531Rapid Gradient Projection Algorithm for Image...

Rapid Gradient Projection Algorithm for Image Reconstruction

Abstract:

In order to improve the speed of compressed sensing image reconstruction algorithm, a rapid gradient projection algorithm for image reconstruction is proposed. In traditional Gradient Projection algorithm, the pursuit direction is alternating, in rapid gradient projection algorithm, we use the Newton's method to calculate the gradient descent direction, thus the constraints conditions of gradient projection is satisfied. And the target function is updated in each iteration computing. The effect of approximation matrix to target function is reduced. The iteration computing times is reduced, because the algorithm works in accurate search direction. Experiment results show that, compared with the GPSR algorithm, the RGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 530-531)

Pages:

443-446

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.530-531.443

Citation:

Cite this paper

Online since:

February 2014

Authors:

Hai Xia Yan*, Yan Jun Liu

Keywords:

Compressed Sensing, Gradient Projection, Image Reconstruction, Signal Processing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] D. Donoho, Compressed sensing[J]. IEEE Trans. Inform. Theory. 2006, 52 (4): 1289–1306.

DOI: 10.1109/tit.2006.871582

Google Scholar

[2] Candès E.J., Romberg,J., Tao,T., Signal recovery from incomplete and inaccurate measurements[J]. Comm. Pure. Appl. Math. 2005, 59(8): 1207-223.

DOI: 10.1002/cpa.20124

Google Scholar

[3] Liu Yong, Wei Dong Hong, Mao Jing Li. A fast compressed sensing reconstruction algorithm based on inner product optimization[J]. Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, v 36, n 1, pp.19-22, February (2013).

Google Scholar

[4] Hernandez, James, Harmany, Zachary, Thompson, Daniel, et al. Bounded gradient projection methods for sparse signal recovery[J]. 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSPp 949-952, (2011).

DOI: 10.1109/icassp.2011.5946562

Google Scholar

[5] Figueiredo, Mário A.T. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal on Selected Topics in Signal Processing, v 1, n 4, pp.586-597, (2007).

DOI: 10.1109/jstsp.2007.910281

Google Scholar