Statistical and Learning Techniques in Computer Vision Lecture 6: Markov Chain Monte Carlo and Gibbs Sampling
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چکیده
Our goal is to produce the best reconstructions of an image given a noisy input image I0. We write any possible reconstruction of the image as a random vector I of pixel values. The best reconstruction is the one that maximizes the posterior probability p(I|I0) = p(I0|I) p(I) (1) This posterior probability is constructed as a Markov Random Field (MRF). More specifically, the random variable I = {ip} represents a sample image (vector of pixel values). Remember, I is in our case a large set of random
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