Detection of Automatic Kikuchi Lines Using Curvelet Transform Based Contrast Enhancement Methods

Abstract

In General, Automated crystal orientation measurement (ACOM) in the scanning electron microscope (SEM) is a standard technique of texture analysis (pattern recognition) that is used in materials science. The measurement is carried out by interpreting backscatter Kikuchi patterns, in particular by the extraction of the position of so-called Kikuchi bands, i.e. pairs of parallel lines. Their detection strongly depends on appropriate processing of a source image, which usually is highly corrupted by noise and has uneven background illumination. Such advanced processing is addressed in this paper and the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal which is applied for removing the false line in the image. It exploits wavelet transform based de-noising as well as curve modification and curvelet transform based contrast enhancement methods. Based on the sufficient analysis in discrete cosine transform (DCT) domain, where each of the coefficients expresses a texture feature in a certain direction, the pseudo- Genetic DCT coefficients are then constructed by appropriately rearranging the Fourier coefficients in terms of their frequency components. The filtering methods used are the arithmetic mean filtering and geometric mean filtering. The pattern generation is obtained by the genetic algorithm which optimizes the pattern generation. The kikuchi lines are formed symmetrically by this process. Finally the kikuchi lines are detected and enhanced by ABF and using multi histogram Equalization