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Binary Image Denoising | Iterated Conditional Modes | Simulated Annealing | MAP | MRF | python
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Binary Image Denoising with greedy ICM (Iterated Conditional Modes) algorithm and global optimization algorithm SA (Simulated Annealing) - Bayesian MAP inference in (discrete) Markov Random Field UGM (undirected graphical model) with Ising Priors, an implementation in python
- ICM is greedy, fast, always rejects locally bad update and converges to local minimum often
- SA has metropolis criteria where it can accept/reject an update, initially ar high temperature accepts lots of bad updates to explore the space (escape from local minimum) and gradually cools down, helping to converge to a better (sometimes global) minimum.
- Pixels are visited in random order
#imageprocessing #imageprocessingpython #python #machinelearning #optimization #algorithm
- ICM is greedy, fast, always rejects locally bad update and converges to local minimum often
- SA has metropolis criteria where it can accept/reject an update, initially ar high temperature accepts lots of bad updates to explore the space (escape from local minimum) and gradually cools down, helping to converge to a better (sometimes global) minimum.
- Pixels are visited in random order
#imageprocessing #imageprocessingpython #python #machinelearning #optimization #algorithm
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