Beneficial Image Preprocessing by Contrast Enhancement Technique for SEM Images

In this paper a morphological filtering algorithm using an exposure thresholding and measures of central tendency has been proposed for solving the low contrast of Scanning Electron Microscopic (SEM) images of composite materials for accurate Filler Content Estimation. SEM image of a composite material comprises visible morphological structures like fillers such as silica nanoparticles. The SEM image analysis via segmentation will assist in the study of distribution of these structures. The estimation of the filler content is more accurate only when the SEM images have proper contrast for analysis if not the results lead to less accuracy. To overcome this drawback, we have proposed a preprocessing technique to increase the contrast of SEM images. So that the preprocessed image can be used for post processing namely segmentation and hence the error is less for filler content estimation. We introduced the transformations using morphological processing to extract the bright and darker features of the images. The optimum threshold value is determined by the image exposure. A detailed comparative analysis with other existing techniques has been performed to prove the superior performance of the proposed method.