Performance Tuning for CUDA-Accelerated Neighborhood Denoising Filters
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}
}
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.
November 9, 2011 by hgpu
