GPU-Oriented Parallel Algorithm for Histogram Statistical Image Enhancement

Abstract

Histogram statistics has important applications in the fields of image enhancement and target detection. However, with the increasing size of the image and the higher real-time requirements, the processing process of the histogram statistical local enhancement algorithm is slow and cannot reach the expected satisfactory speed. In view of this deficiency, this paper realizes the parallel processing of histogram statistical image enhancement algorithm on graphics processing unit (GPU) platform, which improves the processing speed of large format digital images. Firstly, the efficiency of data access is improved by making full use of compute unified device architecture (CUDA) active thread block and active thread to process different sub-image blocks and pixels in parallel. Then, the paralle-lization of histogram statistical image enhancement algorithm on GPU platform is realized by using kernel configu-ration parameter optimization and data parallel computing technology. Finally, the efficient data transmission mode between the host and the device is adopted, which further shortens the execution time of the system on the hetero-geneous computing platform. The results show that for images with different image sizes, the processing speed of the image histogram statistical parallel algorithm is two orders of magnitude higher than that of the CPU serial algorithm. It takes 787.11 ms to process an image with an image size of 3241×3685. The processing speed of the parallel algo-rithm is increased by 261.35 times. It lays a good foundation for the realization of real-time large-scale image processing.

Keywords

• |histogram statistics|local enhancement|local mean|graphics processing unit (gpu)|compute unified device architecture (cuda)|parallel algorithm