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Performance Tuning for CUDA-Accelerated Neighborhood Denoising Filters

Ziyi Zheng, Wei Xu and Klaus Mueller
Center for Visual Computing, Computer Science, Stony Brook University, Stony Brook, NY 11790 USA
Workshop on High Performance Image Reconstruction (HPIR), 2011

@article{zheng2011performance,

   title={Performance Tuning for CUDA-Accelerated Neighborhood Denoising Filters},

   author={Zheng, Z. and Xu, W. and Mueller, K.},

   year={2011}

}

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Neighborhood denoising filters are powerful techniques in image processing and can effectively enhance the image quality in CT reconstructions. In this study, by taking the bilateral filter and the non-local mean filter as two examples, we discuss their implementations and perform fine-tuning on the targeted GPU architecture. Experimental results show that the straightforward GPU-based neighborhood filters can be further accelerated by pre-fetching. The optimized GPU-accelerated denoising filters are ready for plug-in into reconstruction framework to enable fast denoising without compromising image quality.
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