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Acceleration of an improved Retinex algorithm

Yuan-Kai Wang, Wen-Bin Huang
Department of Electrical Engineering, Fu Jen Catholic University, 510, Zhongzheng Rd., Xinzhuang Dist., New Taipei County 24205, Taiwan
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011

@article{wang2011acceleration,

   title={Acceleration of an Improved Retinex Algorithm},

   author={Wang, Y.K. and Huang, W.B.},

   booktitle={IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011},

   year={2011}

}

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Retinex is an image restoration method and the center/surround Retinex is appropriate for parallelization because it utilizes a convolution operation with large kernel size to achieve dynamic range compression and color/lightness rendition. However, its great capability for image enhancement comes with intensive computation. This paper presents a GPURetinex, which is a data parallel algorithm based on GPGPU/CUDA. The GPURetinex exploits GPGPU’s massively parallel architecture and hierarchical memory to improve efficiency. The GPURetinex has been further improved by optimizing the memory usage and out-of-boundary extrapolation in the convolution step. In our experiments, the GPURetinex can gain 72 times speedup compared with the optimized single-threaded CPU implementation by OpenCV for the images with 2048 x 2048 resolution. The proposed method also outperforms a Retinex implementation based on the NPP (nVidia Performance Primitives).
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