scholarly journals Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

2020 ◽  
Vol 59 (3) ◽  
pp. 712
Author(s):  
Mingjie Gao ◽  
Guangtao Si ◽  
Yuanyuan Bai ◽  
Lihong V. Wang ◽  
Chengbo Liu ◽  
...  

2012 ◽  
Vol 69 (1) ◽  
pp. 91-102 ◽  
Author(s):  
Seunghoon Nam ◽  
Mehmet Akçakaya ◽  
Tamer Basha ◽  
Christian Stehning ◽  
Warren J. Manning ◽  
...  


2012 ◽  
Vol 268-270 ◽  
pp. 1706-1709
Author(s):  
Qian Li Wang ◽  
Jian Fu ◽  
Ren Bo Tan ◽  
Li Yuan Chen

Industrial computed tomography (ICT) is an advanced non-contact non-destructive testing technique and plays a key role in many fields. Low imaging efficiency is one of the drawbacks of ICT towards engineering applications. In this paper, we report the design and realization of real-time three-dimensional Visualization System for ICT based on visualization toolkit (VTK) and the graphics processing unit (GPU) technique. It greatly improves the imaging speed by developing the new techniques in three aspects such as image reconstruction, data compression and fast volume rendering with GPU and VTK. It will find applications in three-dimensional ICT systems.



2017 ◽  
Vol 06 (04) ◽  
pp. 1750009
Author(s):  
Jonathan Van Belle ◽  
Richard Armstrong ◽  
James Gain

Deconvolution of native radio interferometric images constitutes a major computational component of the imaging process. An efficient and robust deconvolution operation is essential for reconstruction of the true sky signal from measured telescopic data. The techniques of compressed sensing provide a mathematically-rigorous framework within which to implement deconvolution of images formed from a sparse set of nearly-random measurements. We present an accelerated implementation of the orthogonal matching pursuit (OMP) algorithm (a compressed sensing method) that makes use of graphics processing unit (GPU) hardware. We show that OMP correctly identifies more sources than CLEAN, identifying up to 82% of the sources in 100 test images, while CLEAN only identifies up to 61% of the sources. In addition, the residual after source extraction is [Formula: see text] times lower for OMP than for CLEAN. Furthermore, the graphics implementation of OMP performs around 23 times faster than a 4-core CPU.



2015 ◽  
Vol 10 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Frédéric Magoulès ◽  
Abal-Kassim Cheik Ahamed ◽  
Roman Putanowicz




2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers


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