GPU implementation of Belief Propagation method for Image Restoration using OpenCL

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.