Implementation of Non-Gaussian Markov Model for Image Pixel Brightness Differences

In the paper "What makes a good model of natural images?" presented at CVPR2007, Yair Weiss and William T. Freeman propose a non-Gaussian Markov model that captures the statistical properties of natural images through differences in pixel brightness values. The MATLAB implementation includes key functions for modeling pixel relationships using heavy-tailed distributions rather than traditional Gaussian assumptions, with algorithms designed for efficient parameter estimation through maximum likelihood methods. The model serves as foundation for several computer vision applications including image denoising (using belief propagation algorithms) and image inpainting (through probabilistic reconstruction of missing pixels). The source code provides researchers with working implementations of the core statistical modeling components, featuring modules for training the Markov random field model and applying it to image restoration tasks. The authors also analyze limitations of their approach, particularly regarding computational complexity and generalization across different image types, suggesting directions for future improvements in natural image statistical modeling.