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

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

This video was recorded at 6th IAPR - TC-15 Workshop on Graph-based Representations in Pattern Recognition (GbR), Alicante 2007. 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... Show More

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