基于DCT零系数与局部结构张量的局部模糊检测  

Local Blur Detection Based on DCT Zero Coefficients and Local Structure Tensor

作　　者：王奎奎[1] 玉振明[2] 

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004 [2]梧州学院,广西梧州543000

出　　处：《计算机工程》2017年第6期207-211,218,共6页Computer Engineering

基　　金：国家自然科学基金"光学图像局部模糊检测;分割与应用研究"(61562074);2014年度广西高校科学技术研究项目"压缩感知在视频监控的应用研究"(YB2014356)

摘　　要：针对当前相关图像模糊测量方法不能有效检测纹理平坦清晰区域的问题,提出一种新的图像局部模糊区域检测方法,将其应用于存在运动模糊的静态图像运动目标检测。对图像进行分块操作,计算离散余弦变换后的图像块中零系数的个数,数目较多的为模糊区域,较少的为清晰区域。对被判断为模糊区域的图像块,计算模糊区域中每一个像素点处的局部结构张量,对其进行奇异值分解,根据奇异值局部方向一致性度量准则,获得准确的模糊区域。实验结果表明,与现有的SVD,DCT等方法相比,该方法可以较准确地实现对局部模糊图像的模糊度量。Aiming at the problem of present related image blur measurement method cannot detect regions with flat or clear cextured, a novel approach of regional blur identification is proposed and applied to moving object detection in static images with motion blur. Image patches are got from one image, the number of zero Discrete Cosine Transform （DCT） coefficients at every patch is counted. The high number is regard as clear region, low number is blur region. Local structure tensor of every pixel belonging to blur region got by rough detection is computed. To get local directional coherence of singular values, structure tensor is decomposed by singular value decomposition. Experimental results show that the proposed method measures the local blur more accurately than the existing method.

关 键 词：离散余弦变换 局部结构张量 方向一致性 局部模糊 运动模糊 运动目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]