Locally adaptive learning for translation-variant MRF image priors

Markov random field (MRF) models are a powerful tool in machine vision applications. However, learning the model parameters is still a challenging problem and a burdensome task. The main contribution of this paper is to propose a locally adaptive learning framework. The proposed learning framework is simple and effective learning framework for translation-variant MRF models. The key idea is to use neighboring patches as a locally adaptive training set. We use multivariate Gaussian MRF models for local image prior models. Although the Gaussian MRF models are too simple for whole natural image priors, the locally adaptive framework enables to express the prior distributions of the every observed image. These locally adaptive learning framework and the multivariate Gaussian translation-variant MRF models simplify the learning procedures. This paper also includes other two contributions; a novel iteration framework by updating the prior information, and a simple and intuitive derivation of the well-known bilateral filter. Experimental results of denoising applications demonstrate that the denoising based on the proposed locally adaptive learning framework outperforms existing high-performance denoising algorithms.