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Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.4, No. 7)

Publication Date:

Authors : ; ;

Page : 1103-1112

Keywords : Bilateral filtration; GPU; CUDA; Image Processing; NLM filter(non-local means); KLM filter; frame rate..;

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Obtaining high quality images MR is desirable not only for accurate visual assessment but also faurautomic processing to extract clinically revelent parameters If real-time image processing is required, power and size requirements go up as large data processing computers are required to keep pace with the data. In this paper ,we propose using desktop Graphics Processing Units (GPUs) to shrink the Size, Weight and Power (SWaP) pyramid. Image filtering is one of the most important parts in the image-processing. It takes much more time to performance the convolution in image filtering on CPU since the computation demanding of image filtering is massive. Contrast to CPU, GPU may be a good way to accelerate the image filtering. CUDA(Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA. This paper implements the Bilateral filter, using CUDA enhanced parallel computations. The Bilateral filter allows smoothing images, while preserving edges, in contrast to e.g. the Gaussian filter, which smoothes across edges. While delivering visually stunning results, Bilateral filtering is a costly operation. Using NVidia's CUDA technology the filter can be parallelized to run on the GPU, which allows for fast execution, even for high definition images. In this paper the limitations of bilateral filter is avoided by using NLM filter. Also KLM filter is introduced with NLM filter which increases the frame rate of the filtered image.

Last modified: 2015-07-26 19:14:56