Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Technique for the Numerical Solution of Certain Integral Equations of the First Kind
Journal of the ACM (JACM)
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face image restoration based on statistical prior and image blur measure
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer
IEEE Transactions on Computers
On the equivalence of set-theoretic and maxent MAP estimation
IEEE Transactions on Signal Processing
On denoising and best signal representation
IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections
IEEE Transactions on Image Processing
Removal of compression artifacts using projections onto convex sets and line process modeling
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A VQ-based blind image restoration algorithm
IEEE Transactions on Image Processing
Super-resolution of images based on local correlations
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Approaches analyzing local characteristics of an image prevail in image restoration. However, they are less effective in cases of restoring images degraded by large size point spread functions (PSFs) and heavy noise. The recently proposed learning based approaches perform well on recovering details from images degraded by large size PSFs, yet involves complicated implementation process and high computational expense. In this paper, we propose a hybrid approach to object-based image restoration. This method incorporates common characteristics of images from a class of objects into image restoration. These characteristics are represented as deterministic sets built on principal component analysis (PCA) models. The sets are combined with the observation model represented via a Bayesian approach to constrain the solution. A parallel projection algorithm is also proposed to find the solution that satisfies all constraints. Experiments performed on frontal face images using the proposed approach show superior performance over those based on local analysis in the cases involving large size PSF and heavy noise degradation. Compared with learning based approaches, the proposed approach can be implemented with ease and the solution can be found with less complexity.