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Research on cubic convolution interpolation parallel algorithm based on GPU

The traditional cubic convolution algorithm has to confront with the problems of large operational scale and slow efficiency, when used to realize the remote sensing image magnification. In this paper, GPU, as a burgeoning high performance computing technique, is proposed to parallel processing the traditional cubic convolution, which we call the Cubic Convolution Parallel Algorithm (CCPA). This algorithm that divides the pixels points equally to each block, guarantees each pixel point is executed by a thread and threads are executed simultaneously in GPU, improving the interpolation efficiency greatly. The experimental results show that compared with the traditional cubic convolution algorithm, this algorithm not only increases the calculation speed, but also achieves high quality image after zooming. Meanwhile, with the growth of image resolution, the advantages of the algorithm become more and more obvious, for instance, to the image of 10240 * 10240 resolutions, the speed processed by GPU is 97.7% higher than that by CPU. Moreover, this algorithm also has profound practical value for remote sensing image processing under some emergency situations such as earthquakes, floods and other disasters, with the characteristic of good image quality and realtime mechanism.