Image dehazing algorithm based on improved dark channel prior and heterogeneous optimization
Yin Zhang, Junbo Wang, Guowei Li, Qi Tang, Shibin Xiong, Wei Jiang · 2023
This paper presents an image dehazing algorithm based on the improved dark channel prior and heterogeneous optimization. The improved dark channel prior simplifies the window-based dark channel prior principle to pixel based dark channel prior, and employs heterogeneous acceleration techniques to improve the processing speed. To further optimize the algorithm's performance, we implement the improved dark channel prior dehazing algorithm on CPU, GPU, and FPGA. We compared the processing effects, runtime, and pixel output under unit power consumption of the dark channel prior dehazing methods based on slice bilateral filtering and guided filtering on these different types of processors. The experimental results show that the algorithm proposed in this paper runs faster on GPU and FPGA than on CPU, and also exhibits higher energy efficiency. Moreover, compared to the dark channel prior dehazing methods based on slice bilateral filtering and guided filtering, this algorithm has higher efficiency and lower power consumption while improving the image quality.