目的 目前模糊测量方法难以处理存在纹理平坦区域时的局部模糊测量。针对该问题,提出一种基于BP(back propagation)神经网络的图像局部模糊检测方法。方法 该方法采用所有奇异值以描述不同尺度信息随模糊变化情况,并与描述高频信息变化的DCT(discrete cosine transform)非零系数结合,实现奇异值向量和DCT非零系数个数联合的混合模糊测度,达到空频联合的模糊度描述。在此基础上,通过训练BP神经网络分类器, 实现图像块模糊值预测。结果 单幅局部模糊图像实验中,较好区分纹理平坦区域和模糊区域的模糊程度;多幅局部模糊图像的统计实验中,召回率-准确率(RP)评估曲线显示在相同召回率下准确率较其他方法高。结论 该方法可以较准确地实现局部模糊图像(特别是存在纹理平坦区域的局部模糊图像)的模糊测量。

Objective The existing blur metrics for locally blurred images are difficult to use in the measurement of flat textured areas. Thus, a back propagation(BP) neural network-based image local blur measurement method is proposed to overcome this limitation.Method A new unified blur feature based on all singular values and non-zero discrete cosine transform(DCT) coefficients is presented. This feature measures sharpness from both spatial and frequency domains. Different singular values reflect the distribution of different scale information, which vary differently after blurring.The number of non-zero DCT coefficients depicts the information lost in the high frequency domain. Their combination can capture the blurring effect in the flattened textured area. BP neural network-based classifier is trained to predict the blur measurement of each block on the basis of the metric. Result The method can better distinguish the flat textured areas and blurred areas of a single locally blurred image compared with existing methods. According to the recall-precision curve, the statistical experiment of multiple locally blurred images shows that a higher precision can be obtained with the proposed method than with existing methods. Conclusion Therefore, the proposed method measure the local blur more effectively, particularly that of flat textured areas, than the existing methods can.