Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review
K. P. N. Murthy, M. Janani, B. Shenbga Priya
Abstract
A review of Bayesian restoration of digital images based on Monte Carlo techniques is presented. The topics covered include Likelihood, Prior and Posterior distributions, Poisson, Binay symmetric channel, and Gaussian channel models of Likelihood distribution,Ising and Potts spin models of Prior distribution, restoration of an image through Posterior maximization, statistical estimation of a true image from Posterior ensembles, Markov Chain Monte Carlo methods and cluster algorithms.
Create a lesson
Related papers
UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
Tianjie Ju, Zheng Wu, Yueqing Sun et al.
Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
Chanho Park, Daehyeon Choi, Jihyun Lee et al.
Reconstructing Humans and Objects in Interaction using Large Reconstruction Models
Agniv Chatterjee, Georgios Pavlakos
LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics
Lukas Kuhn, Lucas Maes, Giuseppe Serra et al.
Successive Capacity Growth: Task-Complexity-Driven Width and Depth Expansion for Vision Transformer Encoders in JEPA World Models
Frederik Berenz
KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations
Chenchen Ge, Hanwen Shen, Bowen Jing et al.