Using maximum variance index of fuzziness for contrast enhancement of Nano and micro-images of TEM

Abstract

Transmission electron microscopy (TEM) is one of the most useful methods to clarify the structure in micro and Nano materials. We developed a quantitative analysis method for structure identification of Nano materials containing Nano-space by using electron microscopy combined with a contrast enhancement technique. In this paper an entropic-like index of fuzziness is presented to be an indication of information transfer from a TEM image to its enhanced one. The image is firstly transmitted to fuzzy domain. The membership values are then modified according to a 5-parametric transfer function aiming to maximize the maximum variance index of fuzziness. In the proposed index of fuzziness, the Sugeno class of complement is employed to make the index more adaptable and flexible to various types of applications a TEM image may involve. A common involvement of microscopic image processing techniques is the non-uniform backlight illumination of the images. To this aim, the image is split into sub-images of with quite uniform illumination and then the segments are analyzed separately. An implementation and simulation is performed finally to demonstrate the effectiveness, adaptability and generally applicability of the proposed method in case of microscopic Nano-scale image enhancement.