离焦图像多视角模糊特征自动补偿仿真研究

传统离焦图像多视角模糊特征自动补偿方法存在着图像信息丢失率大、图像补偿完整度低的弊端,为了解决上述问题,提出离焦图像多视角模糊特征自动补偿方法研究。为了得到更好的模糊特征补偿效果,对离焦图像形成过程进行分析,以此为基础,对离焦图像多视角模糊模型进行构建,以构建的离焦图像多视角模糊模型为工具,采用聚类算法对离焦图像模糊特征进行相应的提取,以提取的离焦图像多视角模糊特征为基础,采用补偿算法对离焦图像多视角模糊特征进行处理,实现了离焦图像多视角模糊特征的自动补偿。通过仿真对比实验得到,与现有的三种离焦图像多视角模糊特征自动补偿方法相比较,提出的离焦图像多视角模糊特征自动补偿方法极大的降低了图像信息丢失率,提升了图像补偿完整度,充分说明提出的离焦图像多视角模糊特征自动补偿方法具备更好的补偿性能。

Simulation Study on Automatic Compensation of Multi-View Fuzzy Features of Defocused Image

Due to large image information loss rate and low image compensation integrity in traditional method,this article presented an automatic compensation method for multi-view fuzzy feature of defocused image.In order to get better effect of fuzzy feature compensation,the formation process of defocused image was analyzed.On this basis,the multi-view fuzzy model of defocused image was constructed.Based on the model,the fuzzy features of defocused image were extracted by clustering algorithm,and then the compensation algorithm was used to process multi-view fuzzy features of defocused image.Thus,automatic compensation of multi-view fuzzy feature of defocused image was realized.Simulation results show that,compared with the existing automatic compensation methods,the proposed method greatly reduces the image information loss rate and improves the integrity of image compensation,so that the proposed method has better compensation performance.