Efficient implementation of integrall image algorithm on NVIDIA CUDA

GPU computing makes it possible to perform more efficient implementation results by trying to optimize tasks that require massively parallel computing due to its particular capabilities. This is the main reason for the increase in the number of implemented GPU algorithms. Compared to the CPU, GPU computing has proved its efficiency in accelerating the processing of algorithms. This paper presents an implementation of the integral image algorithm on GPU by using the programming language CUDA. Integral image is important and crucial step in many image-processing algorithms. We also show a comparison between the performance of our algorithm on CPU and GPU on as well as the accelerations obtained. We also compare our algorithm with other GPU implementations using the programming language CUDA. The achieved results show clearly the efficiency of our algorithm. We achieve high speedup results comparing to other CPU and the GPU implementations.