Blind image deblurring is an important but challenging problem in processing. Traditional optimization-based methods typically formulate this task as a maximum-a-posteriori estimation or variational inference problem, whose performance highly relies on handcrafted priors for both the latent and blur kernel. In contrast, recent deep learning generally learn from large collection of training imag...

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully hierarchical statistical framework flux inversion trace gases from flask, in situ, and remotely sensed data. extends the conventional synthesis through consideration correlated error term, capacity online bias correction, provision uncertainty quantification on all unknowns that appear model. We sh...

In this paper, we study the incorporation of Bayesian reg-ularization into constructive neural networks. The degree of regulariza-tion is automatically controlled in the Bayesian inference framework and hence does not require manual setting. Simulation shows that regular-ization, with input training using a full Bayesian approach, produces networks with better generalization performance and low...