Image parallel processing based on GPU
Abstract
In order to solve the compute-intensive character of image processing, based on advantages of GPU parallel operation, parallel acceleration processing technique is proposed for image. First, efficient architecture of GPU is introduced that improves computational efficiency, comparing with CPU. Then, Sobel edge detector and homomorphic filtering, two representative image processing algorithms, are embedded into GPU to validate the technique. Finally, tested image data of different resolutions are used on CPU and GPU hardware platform to compare computational efficiency of GPU and CPU. Experimental results indicate that if data transfer time, between host memory and device memory, is taken into account, speed of the two algorithms implemented on GPU can be improved approximately 25 times and 49 times as fast as CPU, respectively, and GPU is practical for image processing.