title: A Bayesian wavelet-based multidimensional deconvolution with sub-band emphasis.
abstract: This paper proposes a new algorithm for wavelet-based multidimensional image deconvolution which employs subband-dependent minimization and the dual-tree complex wavelet transform in an iterative Bayesian framework. In addition, this algorithm employs a new prior instead of the popular l(1) norm, and is thus able to embed a learning scheme during the iteration which helps it to achieve better deconvolution results and faster convergence.
authors.affiliation: Signal Processing & Communication Group, Dept. of Engineering, University of Cambridge, Cambridge, UK.
authors.forename: Yingsong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Nick
authors.initials: N
authors.lastname: Kingsbury
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D016011:Normal Distribution; D009716:Numerical Analysis, Computer-Assisted; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted