云计算下模糊监控图像变换尺度精准去噪仿真  

Fuzzy Monitoring Image Transform Scale Precision Denoising Simulation under Cloud Computing

作　　者：井荣枝[1] 张志东 JING Rong-zhi;ZHANG Zhi-dong(Sias International College,Zhengzhou University,Zhengzhou Henan 451150,China)

机构地区：[1]郑州大学西亚斯国际学院,河南郑州451150

出　　处：《计算机仿真》2020年第12期349-352,共4页Computer Simulation

基　　金：河南省科技厅科技攻关项目(182102210547);河南省科技厅科技攻关项目(182102210544);河南省高等学校重点科研项目(17A520017)。

摘　　要：针对传统的模糊监控图像变换尺度去噪过程中,存在去噪完成时间较长、能量消耗较大、去噪后图像效果较差等问题。提出基于双树复小波变换的监控图像去噪方法。通过对云计算下模糊监控图像进行分析,采用模糊聚类的方法对监控图像进行分割;结合双树复小波变换方法对分割后的模糊监控图像进行小波变换,根据最大后验概率估计目标图像双树复小波方差,通过维纳滤波得到去噪后的小波系数,以双树复小波反变换得到去噪后的模糊监控图像。实验结果表明,所提方法监控图像去噪完成时间较短、能量消耗较小、去噪后的图像效果较好。In the traditional denoising process,the denoising completion time is too long,and the energy con-sumption is large.Therefore,a method of monitoring image denoising based on the dual-tree complex wavelet trans-form was presented.By analyzing the fuzzy monitoring image under cloud computing,the fuzzy clustering method was used to segment the monitoring image.On this basis,the dual-tree complex wavelet transform method was used to transform the segmented fuzzy monitoring image.According to the maximum posterior probability,the dual-tree complex wavelet variance of target image was estimated.Then,the wavelet coefficients after the image denoising were obtained by Wiener filtering.Finally,the denoised fuzzy monitoring image was obtained by dual-tree complex wave-let inverse transform.Simulation results show that the proposed method needs less time and less energy consumption to complete the image denoising.Meanwhile,the image denoising effect is better.

关 键 词：云计算 模糊监控图像 变换尺度 去噪 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]