Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Markov random field modeling in computer vision
Markov random field modeling in computer vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
A Variable Window Approach to Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov Pixon Information Approach for Low-Level Image Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Piecewise Approximation of Pictures Using Maximal Neighborhoods
IEEE Transactions on Computers
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
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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.