scholarly journals Improvement of Reconstructed Image Quality based on PSF correction in Wavefront Coding by Using Cubic-Phase-Modulation of Coma Aberration

2015 ◽  
Vol 69 (3) ◽  
pp. J91-J97
Author(s):  
Shunichi Sato ◽  
Kazuki Saito ◽  
Mito Ohkura ◽  
Daisuke Sugimura ◽  
Takayuki Hamamoto
Keyword(s):  
Image Quality ◽  
Phase Modulation ◽  
Reconstructed Image ◽  
Cubic Phase ◽  
Wavefront Coding ◽  
Coma Aberration

Method for Improving Reconstructed Image Quality of Digital Hologram Based on SRAD and NSCT

2014 ◽  
Vol 41 (2) ◽  
pp. 0209024
Author(s):  
吴一全 Wu Yiquan ◽  
殷骏 Yin Jun ◽  
朱丽 Zhu Li ◽  
叶志龙 Ye Zhilong
Keyword(s):  
Image Quality ◽  
Reconstructed Image ◽  
Digital Hologram

scholarly journals Eliminating Decoherence Effects and Improving Reconstructed Image Quality in Soft X-ray Ptychography.

2018 ◽  
Vol 24 (S2) ◽  
pp. 44-45
Author(s):  
Chunpeng Wang ◽  
Zijian Xu ◽  
Haigang Liu ◽  
Xulei Tao ◽  
Renzhong Tai
Keyword(s):  
Image Quality ◽  
Reconstructed Image ◽  
X Ray

Analysis of wavefront coding imaging with cubic phase mask decenter and tilt

2016 ◽  
Vol 55 (25) ◽  
pp. 7009 ◽  
Author(s):  
Yijian Wu ◽  
Liquan Dong ◽  
Yuejin Zhao ◽  
Ming Liu ◽  
Xuhong Chu ◽  
...  
Keyword(s):  
Phase Mask ◽  
Cubic Phase ◽  
Wavefront Coding

An Adaptive Regularized Fast Iterative Shrinkage-Thresholding Algorithm for Image Reconstruction in Compressed Sensing

2013 ◽  
Vol 710 ◽  
pp. 593-597
Author(s):  
Xin Meng ◽  
Shi Fang Duan ◽  
She Xiang Ma
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Objective Function ◽  
Time Complexity ◽  
Regularization Parameter ◽  
Iteration Process ◽  
Reconstructed Image ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

Aiming at the problems of worse reconstructed image quality and larger time complexity of the fast iterative shrinkage-thresholding algorithm in compressed sensing, this paper presents adaptive regularized fast iterative shrinkage-thresholding algorithm. This algorithm brings in the idea of adaptively selecting regularization parameter on the basis of using gradient method and threshold shrinkage to minimize the objective function. During the iteration process regularization parameter is adaptively selected from the whole value in order to adjust the proportion of the former part and the latter part of the objective function value. Simulation results show that the proposed algorithm, compared with the traditional algorithms, obtains the better reconstructed image quality and lower time complexity.

Sign in / Sign up

Export Citation Format

Share Document