基于小波变换和神经网络的数字图像清晰度识别算法

作者：沈国鑫,陈国金,费海波 日期：2008-04-23/span> 浏览：3970 查看PDF文档

基于小波变换和神经网络的数字图像清晰度识别算法
沈国鑫,陈国金,费海波
(杭州电子科技大学 机械工程学院，浙江 杭州 310037)

摘 要：数字图像的清晰度评价一直是各类数字成像系统的一个关键问题。在研究了常用的清晰度评价方法的基础上，通过研究找出了图像二维小波分解向量与图像质量的对应关系，分析了其细节信息，并采用统计的方法提取了图像特征二层小波分量的统计值作为分析特征，然后构建了3层BP神经网络模型对图像清晰度进行识别，得到图像质量等级。仿真实验结果表明，这种方法可以达到较好的图像识别率。
关键词：小波分析；清晰度；神经网络；图像识别

Digital image quality recognition algorithm based on neural network and wavelet transformation
SHEN Guoxin, CHEN Guojin, FEI Haibo
(College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310037, China)

Abstract: Evaluation of definition for gray scale digital image is an important aspect of digital imaging system. After the commonly sharpness function was analysised, the relations between twodimensional wavelet decomposition vector and the picture quality were studied and discovered, and the image characteristics from the detail information through statistics analysis were abstracted. Then 3 BP nerve network model for quality pattern recognition was costructed. The recognition rate of this means is good.
Key words: wavelet analysis; sharpness; neural network; image recognition
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