Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images - 2012 PROJECT TITLE :Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images - 2012ABSTRACT: Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an appropriate dictionary for the data under test and also for image recovery. In the context of compressive sensing, significant improvements in image recovery are manifested using learned dictionaries, relative to using standard orthonormal image expansions. The compressive-measurement projections are also optimized for the learned dictionary. Additionally, we consider simpler (incomplete) measurements, defined by measuring a subset of image pixels, uniformly selected at random. Spatial interrelationships within imagery are exploited through use of the Dirichlet and probit stick-breaking processes. Several example results are presented, with comparisons to other methods in the literature. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Monotonic Regression A New Way for Correlating Subjective and Objective Ratings in Image Quality - 2012 Patch-Based Near-Optimal Image Denoising - 2012
