Deducing Local Influence Neighbourhoods With Application to Edge-Preserving Image Denoising

author: Ashish Raj, University of California, San Francisco
published: July 9, 2007,   recorded: June 2007,   views: 5971


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Traditional image models enforce global smoothness, and more recently Markovian Field priors. Unfortunately global models are inadequate to represent the spatially varying nature of most images, which are much better modeled as piecewise smooth. This paper advocates the concept of local influence neighbourhoods (LINs). The influence neighbourhood of a pixel is defined as the set of neighbouring pixels which have a causal influence on it. LINs can therefore be used as a part of the prior model for Bayesian denoising, deblurring and restoration. Using LINs in prior models can be superior to pixel-based statistical models since they provide higher order information about the local image statistics. LINs are also useful as a tool for higher level tasks like image segmentation. We propose a fast graph cut based algorithm for obtaining optimal influence neighbourhoods, and show how to use them for local filtering operations. Then we present a new expectation-maximization algorithm to perform locally optimal Bayesian denoising. Our results compare favourably with existing denoising methods.

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Reviews and comments:

Comment1 Deepika Jha, July 14, 2007 at 5:21 p.m.:

I am certain that anyone in this field would appreciate your work. Nice job with the slides and the informative talk. Overall, very nice Presentation!

Comment2 Nimisha Jha, July 20, 2009 at 12:18 p.m.:

Excellent presentation.....Wow!That's a lot of hard work...Keep it up....I'm sure u make everyone very proud of you.All the best

Comment3 sridevi, December 19, 2009 at 11:15 a.m.:

i have seen your seminar first of all the seminar u have given is very valuable for me but i want some more information from you that is iam doing mtech project on digital image processing and for this i want to know about fuzzy theory and about the paper from IEEE "noise removal by using fuzzy image filtering"
can u plz help me

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