Markov random field modeling in computer vision
Markov random field modeling in computer vision
Markov chain Monte Carlo methods with applications to signal processing
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Super-Resolution Imaging
Guest Editors' Introduction: The Top 10 Algorithms
Computing in Science and Engineering
Super-Resolution from Image Sequences - A Review
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Superresolution imaging: Theory, Algorithms and Applications
Multidimensional Systems and Signal Processing
Super-resolution using hidden Markov model and Bayesian detection estimation framework
EURASIP Journal on Applied Signal Processing
A frequency domain approach to registration of aliased images with application to super-resolution
EURASIP Journal on Applied Signal Processing
Robust fusion of irregularly sampled data using adaptive normalized convolution
EURASIP Journal on Applied Signal Processing
Editorial: super-resolution imaging: analysis algorithms, and applications
EURASIP Journal on Applied Signal Processing
A soft MAP framework for blind super-resolution image reconstruction
Image and Vision Computing
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
MCMC for joint noise reduction and missing data treatment indegraded video
IEEE Transactions on Signal Processing
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
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Hierarchical Bayesian image restoration from partially known blurs
IEEE Transactions on Image Processing
Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
A closed form algorithm for superresolution
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Simultaneous image interpolation for stereo images
Signal Processing
Image super-resolution: use of self-learning and gabor prior
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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The objective of super-resolution (SR) imaging is to reconstruct a single higher-resolution image based on a set of lower-resolution images that were acquired from the same scene to overcome the limitations of image acquisition process for facilitating better visualization and content recognition. In this paper, a stochastic Markov chain Monte Carlo (MCMC) SR image reconstruction approach is proposed. First, a Bayesian inference formulation, which is based on the observed low-resolution images and the prior high-resolution image model, is mathematically derived. Second, to exploit the MCMC sample-generation technique for the stochastic SR image reconstruction, three fundamental issues are observed as follows. First, since the hyperparameter value of the prior image model controls the degree of regularization and intimately affects the quality of the reconstructed high-resolution image, how to determine an optimal hyperparameter value for different low-resolution input images becomes a very challenging task. Rather than exploiting the exhaustive search, an iterative updating approach is developed in this paper by allowing the value of hyperparameter being simultaneously updated in each sample-generation iteration. Second, the samples generated during the so-called burn-in period (measured in terms of the number of samples initially generated) of the MCMC-based sample-generation process are considered unreliable and should be discarded. To determine the length of the burn-in period for each set of low-resolution input images, a time-period bound in closed form is mathematically derived. Third, image artifacts could be incurred in the reconstructed high-resolution image, if the number of samples (counting after the burn-in period) generated by the MCMC-based sample-generation process is insufficient. For that, a variation-sensitive bilateral filter is proposed as a 'complementary' post-processing scheme, to improve the reconstructed high-resolution image quality, when the number of samples is insufficient. Extensive simulation results have clearly shown that the proposed stochastic SR image reconstruction method consistently yields superior performance.