Parallel implementation and optimization of high definition video real-time dehazing

In some warning applications, such as aircraft taking-off and landing, ship sailing, and traffic guidance in foggy weather, the high definition (HD) and rapid dehazing of images and videos is increasingly necessary. Existing technologies for the dehazing of videos or images have not completely exploited the parallel computing capacity of modern multi-core CPU and GPU, and leads to the long dehazing time or the low frame rate of video dehazing which cannot meet the real-time requirement. In this paper, we propose a parallel implementation and optimization method for the real-time dehazing of the high definition videos based on a single image haze removal algorithm. Our optimization takes full advantage of the modern CPU+GPU architecture, which increases the parallelism of the algorithm, and greatly reduces the computational complexity and the execution time. The optimized OpenCL parallel implementation is integrate into FFmpeg as an independent module. The experimental results show that for a single image, the performance of the optimized OpenCL algorithm is improved approximately 500% compared with the existing algorithm, and approximately 153% over the basic OpenCL algorithm. The 1080p (1920?×?1080) high definition hazy video can also processed at a real-time rate (more than 41 frames per second).