Markov random field modeling in computer vision
Markov random field modeling in computer vision
Super-Resolution Imaging
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
IEEE Transactions on Image Processing
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
Spatially varying regularization of image sequences super-resolution
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Simultaneous image interpolation for stereo images
Signal Processing
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In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully exploited and shown as an effective approach to perform super-resolution image reconstruction. However, one major challenge lies at the selection of the hyperparameter of the prior image model, which affects the degree of regularity imposed by the prior image model, and consequently, the quality of the estimated high-resolution image. To tackle this challenge, in this paper, we propose a novel approach to automatically adapt the model’s hyperparameter during the MCMC process, rather than the exhaustive, off-line search. Experimental results presented show that the proposed hyperparameter adaptation method yields extremely close performance to that of the optimal prior image model case.