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.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. 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. Therefore

the proposed method measure the local blur more effectively

particularly that of flat textured areas

than the existing methods can.