Comparison of Denoising Filters on Greyscale TEM Image for Different Noise

Abstract

TEM (Transmission Electron Microscopy) are currently the most widely used techniques to study nanoparticles morphology. Removal of noise from an image is one of the most important tasks in image processing. Depending on the nature of the noise, such as additive or multiplicative type of noise, there are several approaches towards removing noise from an image. Image De-noising improves the quality of images acquired by optical, electro-optical or electronic microscopy. This paper compares five filters on the measures of mean of image, signal to noise ratio, peak signal to noise ratio & mean square error. In this work four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise. Further results have been compared for all noises. . In this paper four types of noise are used and image de-noising performed for different noise by various filters (WFDWT, BF, HMDF, FDE, and DVROFT). Further results have been compared for all noises. It is observed that for Gaussian Noise WFDWT & for other noises HMDF has shown the better performance results. Keyword: Nonmaterial, Noise, Denoising, Filters, Quality I. Introduction Image denoising can be considered as a component of processing or as a process itself. Image denoising involves the manipulation of the image data to produce a visually high quality image. Images get often corrupted by additive and multiplicative noise. In today's real time applications and requirements resolution we get from normal images is not sufficient. We need look insight its crystallographic structure, topography, morphology etc of a substance. As nanoscopic image has got wide and significant use in the medical research and applications and in many other domains. Due to acquisition TEM images contain electronic noise and white diffraction artifacts localized on the edges of the Nanomaterials Various types of filters have been proposed for removal of noise in these microscopic images. This paper discusses the major types of noise used in simulation in the first part, few types of filters being simulated on a nanoscopic image in the second part and comparative analysis in the third part.