Modulation spread function (MTF) is the function of spatial frequency used to evaluate the spatial quality of an imaging system. In measuring the MTF of the digital images, the slanted edge method is widely used. The traditional method projects the pixel values to the direction perpendicular to the edge and construct the edge spread function (ESF) in a subpixel resolution by binning pixels. However, the pixels within in a subpixel bin is not always distributed uniformly, which introduces errors in measurement results. Meanwhile, the value of an image pixel may deviate from the exact value of the ESF on the position of the pixel center since it is the integral of a short segment of the continuous ESF. Besides, the sampling interval is compressed due to inclination, so that the scale in the frequency domain is changed. In this paper, a novel measurement method which samples the ESF via weighting by distance is proposed and the errors exist in the traditional method is compensated. The edge location estimation method is also improved. Simulation experiments on different edge angles and image noise levels are conducted. The results show that the proposed method has an excellent performance on measuring accuracy and robustness.
An imaging system’s spatial quality can be expressed by the system’s modulation spread function (MTF) as a function of
spatial frequency in terms of the linear response theory. Methods have been proposed to assess the MTF of an imaging
system using point, slit or edge techniques. The edge method is widely used for the low requirement of targets. However,
the traditional edge methods are limited by the edge angle. Besides, image noise will impair the measurement accuracy,
making the measurement result unstable. In this paper, a novel measurement method based on the support vector
machine (SVM) is proposed. Image patches with different edge angles and MTF levels are generated as the training set.
Parameters related with MTF and image structure are extracted from the edge images. Trained with image parameters
and the corresponding MTF, the SVM classifier can assess the MTF of any edge image. The result shows that the
proposed method has an excellent performance on measuring accuracy and stability.