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Proceedings Article

Acceleration of the Retinex algorithm for image restoration by GPGPU/CUDA

[+] Author Affiliations
Yuan-Kai Wang, Wen-Bin Huang

Fu Jen Catholic Univ. (Taiwan)

Proc. SPIE 7872, Parallel Processing for Imaging Applications, 78720E (January 25, 2011); doi:10.1117/12.876640
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From Conference Volume 7872

  • Parallel Processing for Imaging Applications
  • John D. Owens; I-Jong Lin; Yu-Jin Zhang; Giordano B. Beretta
  • San Francisco Airport, California, USA | January 23, 2011

abstract

Retinex is an image restoration method that can restore the image's original appearance. The Retinex algorithm utilizes a Gaussian blur convolution with large kernel size to compute the center/surround information. Then a log-domain processing between the original image and the center/surround information is performed pixel-wise. The final step of the Retinex algorithm is to normalize the results of log-domain processing to an appropriate dynamic range. This paper presents a GPURetinex algorithm, which is a data parallel algorithm devised by parallelizing the Retinex based on GPGPU/CUDA. The GPURetinex algorithm exploits GPGPU's massively parallel architecture and hierarchical memory to improve efficiency. The GPURetinex algorithm is a parallel method with hierarchical threads and data distribution. The GPURetinex algorithm is designed and developed optimized parallel implementation by taking full advantage of the properties of the GPGPU/CUDA computing. In our experiments, the GT200 GPU and CUDA 3.0 are employed. The experimental results show that the GPURetinex can gain 30 times speedup compared with CPU-based implementation on the images with 2048 x 2048 resolution. Our experimental results indicate that using CUDA can achieve acceleration to gain real-time performance.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Yuan-Kai Wang and Wen-Bin Huang
"Acceleration of the Retinex algorithm for image restoration by GPGPU/CUDA", Proc. SPIE 7872, Parallel Processing for Imaging Applications, 78720E (January 25, 2011); doi:10.1117/12.876640; http://dx.doi.org/10.1117/12.876640


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