A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression computation computation using iteratively reweighted least squares
SIAM Journal on Matrix Analysis and Applications
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Minimax entropy principle and its application to texture modeling
Neural Computation
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
International Journal of Computer Vision
Introducing dynamic prior knowledge to partially-blurred image restoration
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Double regularized bayesian estimation for blur identification in video sequences
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Deblurring is important in many visual systems. This paper presents a novel approach for nonstationary blurred image reconstruction with ringing reduction in a variational Bayesian learning and regularization framework. Our approach makes effective use of the image statistical prior and image local spatial conditions through the whole learning scheme. A nature image statistics based marginal prior distribution is used not only for blur kernel estimation but also for image reconstruction. For an ill-posed blur estimation problem, variational Bayesian ensemble learning can achieve a tractable posterior using an image statistic prior which is translation and scale-invariant. During the deblurring, nonstationary blurry images have stronger ringing effects. We thus propose an iterative reweighted regularization function based on the use of an image statistical prior and image local spatial conditions for perceptual image deblurring.