Visual reconstruction
Statistical Inference for Spatial Processes
Statistical Inference for Spatial Processes
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
A Full Bayesian Approach to Curve and Surface Reconstruction
Journal of Mathematical Imaging and Vision
Astronomical image restoration using an improved anisotropic diffusion
Pattern Recognition Letters
Blind image deblurring driven by nonlinear processing in the edge domain
EURASIP Journal on Applied Signal Processing
Bayesian image recovery for dendritic structures under low signal-to-noise conditions
IEEE Transactions on Image Processing
Maximum a posteriori video super-resolution using a new multichannel image prior
IEEE Transactions on Image Processing
Effective image restorations using a novel spatial adaptive prior
EURASIP Journal on Advances in Signal Processing
| Hi-index | 0.14 |
In an image restoration problem one usually has two different kinds of information. In the first stage, one has knowledge about the structural form of the noise and local characteristics of the restoration. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on the hyperparameters, where information about those hyperparameters is included. In this work the author applies the hierarchical Bayesian approach to image restoration problems and compares it with other approaches in handling the estimation of the hyperparameters.