Medical Image Compressed Sensing Reconstruction

2014 ◽  
Vol 556-562 ◽  
pp. 4835-4838 ◽  
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu ◽  
Yu Ming Sun
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Medical Image ◽  
Reconstruction Algorithm ◽  
Gradient Projection ◽  
Sparse Reconstruction ◽  
Image Reconstruction Algorithm ◽  
Gradient Direction ◽  
Medical Image Reconstruction ◽  
Negative Gradient

In order to improve the speed of compressed sensing image reconstruction algorithm, a two step rapid gradient projection for sparse reconstruction in medical image reconstruction is proposed. in traditional gradient projection for sparse reconstruction algorithm, the searching direction is alternate between the negative gradient direction when the direction is ill, the searching speed is slow. Now we search with two step gradient projection, the speed is increased when meets the ill-condition. Compared with the original GPSR algorithm, the TSGPSR algorithm not only accelerate the speed of operation, but also improves the accuracy of the reconstruction. and exhibits higher robustness under different noise intensities.

Improved Gradient Projection Algorithm for Compressed Sensing

2014 ◽  
Vol 543-547 ◽  
pp. 2623-2626
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Reconstruction Accuracy ◽  
Gradient Projection Algorithm ◽  
Gradient Direction ◽  
Searching Method ◽  
Negative Gradient

In order to improve efficient of compressed sensing image reconstruction, an improved gradient projection algorithm of compressed sensing theory is proposed. In improved Gradient Projection algorithm, the pursuit direction is updated by search at negative gradient direction, thus the gradient direction is a single direction, because the traditional gradient projection algorithm searching at alternating searching method ,the efficient of gradient projection algorithm is higher than the traditional gradient projection algorithm, Experiment results show that, compared with the GPSR algorithm, the IGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

Rapid Gradient Projection Algorithm for Image Reconstruction

2014 ◽  
Vol 530-531 ◽  
pp. 443-446
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Descent ◽  
Target Function ◽  
Reconstruction Algorithm ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Descent Direction ◽  
Reconstruction Accuracy ◽  
Gradient Projection Algorithm

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.

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