Speckleless reconstructed image display using convergent light in CGH

2021 ◽  
Author(s):  
Tatsuya Oshinomi ◽  
Masaya Mitobe ◽  
Yuji Sakamoto
Keyword(s):  
Image Display ◽  
Reconstructed Image

Influencing Parameters Of Image Display Acceleration

2014 ◽  
Vol 2 (4) ◽  
pp. 20-30
Author(s):  
N. Harathi ◽  
◽  
V. Meenakshi ◽  
C.H. Suneetha ◽  
◽  
...  
Keyword(s):  
Image Display ◽  
Influencing Parameters

CT of acetabular fractures: software and image display

1982 ◽  
Vol 139 (2) ◽  
pp. 416-417
Author(s):  
BD Toombs ◽  
EN Rayschkolb ◽  
BJ Gibbs
Keyword(s):  
Image Display ◽  
Acetabular Fractures

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

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