Redundancy computation analysis and implementation of phase diversity based on GPU

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• 作者：Quan Zhang ; Hua Bao ; Changhui Rao ; Zhenming Peng
• 关键词：Phase diversity ; Adaptive optics ; Wavefront sensing ; Image restoration ; Parallel computing
• 刊名：Optical Review
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：22
• 期：5
• 页码：741-752
• 全文大小：1,783 KB
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• 作者单位：Quan Zhang (1) (2) (3) (4)
  Hua Bao (1) (3)
  Changhui Rao (1) (3)
  Zhenming Peng (2)
  
  1. Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, 610209, China
  2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, 610054, China
  3. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China
  4. University of Chinese Academy of Sciences, Beijing, 100039, China
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Electromagnetism, Optics and Lasers
  
• 出版者：The Optical Society of Japan, co-published with Springer-Verlag GmbH
• ISSN：1349-9432

文摘

Phase diversity method is not only used as an image restoration technique, but also as a wavefront sensor. However, its computations have been perceived as being too burdensome to achieve its real-time applications on a desktop computer platform. In this paper, the implementation of the phase diversity algorithm based on graphic processing unit (GPU) is presented. The redundancy computations for the pupil function, point spread function, and optical transfer function are analyzed. Two kinds of implementation methods based on GPU are compared: one is the general method which is accomplished by GPU library CUFFT without precision loss (method-1) and the other one performed by our own custom FFT with little damage of precision considering the redundant calculations (method-2). The results show the cost and gradient functions can be speeded up by method-2 in contrast with the method-1 and the overhead of global memory access by kernel fusion can be reduced. For the image of 256 × 256 with the sampling factor of 3, the results reveal that method-2 achieves speedup of 1.83× compared with method-1 when the central 128 × 128 pixels of the point spread function are used. Keywords Phase diversity Adaptive optics Wavefront sensing Image restoration Parallel computing