GPU implementation of Belief Propagation method for Image Restoration using OpenCL

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2014-12-01 DOI:10.1109/ICCCT2.2014.7066721

P. Ravibabu, K. S. Rao, Mallesham Dasari

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引用次数: 1

Abstract

The image processing applications involve huge amount of computational complexity as the operations are carried out on each pixel of the image. The General Purpose computations that are data independent can run on Graphics Processing Units (GPU) to enable speedup in running time due to high level of parallelism. Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) programming environments are well known parallel programming languages for GPU-based Single Instruction Multiple Data (SIMD) architectures. This paper presents parallel implementation of Belief Propagation (BP) algorithm for Image Restoration on GPU using OpenCL parallel programming environment. The experimental results shows that, GPU-based implementation improves the running time of BP for image restoration when compared to sequential implmentation of BP. The best and average running time of BP algorithm on GPUs with 14 multiprocessors (48 cores) is 0.81ms and 1.46ms when tested on various benchmark images with CIF and VGA resolution.

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基于OpenCL的图像恢复信念传播方法的GPU实现

图像处理应用涉及大量的计算复杂性，因为操作是在图像的每个像素上进行的。与数据无关的通用计算可以在图形处理单元(GPU)上运行，从而由于高度并行性而加快运行时间。计算统一设备架构(CUDA)和开放计算语言(OpenCL)编程环境是众所周知的基于gpu的单指令多数据(SIMD)架构的并行编程语言。本文利用OpenCL并行编程环境在GPU上并行实现图像恢复中的BP算法。实验结果表明，与序列BP相比，基于gpu的BP算法提高了BP图像恢复的运行时间。在CIF和VGA分辨率的各种基准图像上测试，BP算法在14个多处理器(48核)gpu上的最佳和平均运行时间分别为0.81ms和1.46ms。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 RIVF International Conference on Computing and Communication Technologies (RIVF)

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