Abstract
The edge of image is one of the important features of the image, edge detection is an important means to extract image features. As the most popular high-performance processing technology, GPU parallel technology is on of the best choices for parallel Prewitt algorithm implementation. Since conventional Prewitt algorithm based upon CPU is computationally intensive, time-consuming, its application is very restricted. In order to improve the efficiency of Prewitt algorithm, CUDA-based parallel Prewitt algorithm and fast imaging algorithm are applied to get higher speedup. Finally, an effective method is proposed by turning the GPR field data into gray-scale image data, then implementation of GPR field data processing with the Prewitt algorithm based upon CUDA. Numerical results on GPR field data have shown that the algorithm is not only of high efficiency, but effective to improve target identification capability based upon GPR.
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