A parallel Non-Local means denoising algorithm implementation with OpenMP and OpenCL on Intel Xeon Phi Coprocessor

Abstract

The Non-Local means (NLM) denoising algorithm calculates similarity weight between denoising pixels and searching area pixels by establishing similar functions. In texture denoising and edge region denoising domain, the Non-Local Means denoising algorithm performs better than many other existing denoising algorithms because it uses the redundant information of images. However, NLM algorithm has defect in speed for the huge computational amount. Recently, Intel Xeon Phi Coprocessor (based on Intel Many Integrated Core architecture, MIC) exhibits huge superiority in speedup computation. Therefore we design parallel algorithm strategies of OpenMP and OpenCL based on the serial NLM algorithm for MIC architecture, and conduct the experiment on CPU, GPU, and MIC with images of different sizes. The experiment suggests that the OpenMP-based NLM algorithm has better performance on Xeon Phi 7120 than on Xeon E5 2692 when the image size is greater than or equal to 1024*1024, the OpenCL-based NLM algorithm has better performance on Xeon Phi 7120 than on NVIDIA Kepler K20M GPU, and OpenCL-based NLM algorithm performs a little better than OpenMP-based NLM algorithm when they both implemented on Intel Xeon Phi 7120.