A K-nearest neighbor-based method for the restoration of damaged images
Pattern Recognition
Bayesian Estimation for Homogeneous and Inhomogeneous Gaussian Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric regression using linear combinations of basis functions
Statistics and Computing
Automated Smoothing of Image and Other Regularly Spaced Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Parallelizing MCMC for Bayesian spatiotemporal geostatistical models
Statistics and Computing
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
Robust B-spline image modeling with application to image processing
IEEE Transactions on Image Processing
On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach
IEEE Transactions on Image Processing
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We address the problem of automatically identifying and restoring damaged and contaminated images. We suggest a novel approach based on a semi-parametric model. This has two components, a parametric component describing known physical characteristics and a more flexible non-parametric component. The latter avoids the need for a detailed model for the sensor, which is often costly to produce and lacking in robustness. We assess our approach using an analysis of electroencephalographic images contaminated by eye-blink artefacts and highly damaged photographs contaminated by non-uniform lighting. These experiments show that our approach provides an effective solution to problems of this type.