COMPARISON OF DENOISING FILTERS ON COLOUR TEM IMAGE FOR DIFFERENT NOISE

TEM (Transmission Electron Microscopy) is an important morphological characterization tool for Nanomaterials. Quite often a microscopy image gets corrupted by noise, which may arise in the process of acquiring the image, or during its transmission, or even during reproduction of the image. Removal of noise from an image is one of the most important tasks in image processing. Denoising techniques aim at reducing the statistical perturbations and recovering as well as possible the true underlying signal. 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 paper four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by various filters (WFDWT, BF, HMDF, FDE, 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

A principled approach to image denoising with similarity kernels involving patches

Denoising is a cornerstone of image analysis and remains a very active research field. This paper deals with image filters that rely on similarity kernels to compute weighted pixel averages. Whereas similarities have been based on the comparison of isolated pixel values until recently, modern filters extend the paradigm to groups of pixels called patches. Significant quality improvements result from the mere replacement of pixel differences with patch-to-patch comparisons directly into the filter. Our objective is to cast this generalization within the framework of mode estimation. Starting from objective functions that are extended to patches, this leads us to slightly different formulations of filters proposed in the literature, such as the local M-smoothers, bilateral filters, and the nonlocal means. A fast implementation of these new filters relying on separable linear-time convolutions is detailed. Experiments show that this principled approach further improves the denoising quality without increasing the computational complexity. (C) 2010 Elsevier B.V. All rights reserved