Li Peng
1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 3.Binjiang College,Nanjing University of Information Science and Technology, Wuxi 214105, ChinaDing Qianwen
1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 3.Binjiang College,Nanjing University of Information Science and Technology, Wuxi 214105, China
TP391.4
Aiming at the disadvantages of large amount of calculation and low time efficiency of traditional maximum inter class difference method (OSTU) in image segmentation, a sparrow optimized OSTU segmentation method (SRWSSA) based on singer chaotic map and random walk strategy is proposed. Firstly, singer chaotic map is used to improve the initialization sparrow population, increase the diversity of initialization population and improve the global search ability; Secondly, the random walk strategy is used to perturb and mutate the updated optimal sparrow, so as to further increase the population diversity and enhance the local search ability; Finally, the standard image is segmented by two-dimensional OSTU using the proposed optimization algorithm to obtain the optimal threshold segmentation image. The SRWSSA algorithm proposed in this paper has significantly improved the optimization ability and iteration time. Compared with PSO-OSTU and SSA-OSTU, the number of iterations is reduced by 83.3% and 76% respectively. The image peak signal-to-noise ratio is increased by 8.2% and 11.3% respectively, and the running time is also improved. Practice shows that this method is feasible.
