This paper presents an outline for removing noise in digital images. We see that the real time images are get more corrupted while acquisition, processing and transmission. As we deal 98% of our daily work on real time application data. So to suppress noise and elimination of noise from real time images is a research area for the developers, that how to predict noise and to remove that one nois...

An automated visual printed circuit board (PCB) inspection system proposed in this paper is designed specifically to detect various types of defects occurred during circuit printing process in manufactory. Two-dimensional HAAR wavelet transform is incorporated in the PCB inspection algorithm of the inspection system. During the inspection, two type of images are required; reference and tested P...

Elimination of noise is a necessary and challengeable operation in image processing. Before performing any operation, images have to be first restored. Images are corrupted by noise during image acquirement and transmission. Noise and blurring effects always corrupts any recorded image. To reduce the impulse noise level in digital images a novel algorithm FSM(Fuzzy Switching Median) filter is p...

The background noise is one of the major factors, which adversely affects the perceived grade of service in audio communication systems. The main problem in most of the environmental noise reduction system is source of noise signal which is to be used as a reference signal. Once the noise source is known then the noise elimination process will become easier. In any environmental conditions, pre...

There are two major routes to address linear inverse problems. Whereas regularization-based approaches build estimators as solutions of penalized regression optimization problems, Bayesian estimators rely on the posterior distribution of the unknown, given some assumed family of priors. While these may seem radically different approaches, recent results have shown that, in the context of additi...

NASA Tech Briefs, November 2010 yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior. This work was done by Baris I. Erkmen and Bruce E. Moision of Caltech for NASA’s Jet Propulsion...

In this paper we introduce the recent normal inverse Gaussian (NIG) probability density as a new model for sparsely coded data. The NIG density is a flexible, four-parameter density, which is highly suitable for modeling unimodal super-Gaussian data. We demonstrate that the NIG density provides a very good fit to the sparsely coded data, obtained here via an independent component analysis (ICA)...

We show that the increments of generalized Wiener process, useful to describe nonGaussian white noise sources, have the properties of infinitely divisible random processes. Using functional approach and the new correlation formula for non-Gaussian white noise we derive directly from Langevin equation, with such a random source, the Kolmogorov’s equation for Markovian non-Gaussian process. From ...