An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Automatic Mosaicing with Super-Resolution Zoom
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Motion-Free Super-Resolution
Hi-index | 0.00 |
In this paper we present a model based approach for super-resolving an image from a sequence of zoomed observations. From a set of images taken at different camera zooms, we super-resolve the least zoomed image at the resolution of the most zoomed one. Novelty of our approach is that decimation matrix is estimated from the given observations themselves. We model the most zoomed image as an autoregressive (AR) model, learn the parameters and use in regularization to super-resolve the least zoomed image. The AR model is computationally less intensive as compare to Markov Random Field (MRF) model hence the approach can be employed in real-time applications. Experimental results on real images with integer zoom settings are shown. We also show how the learning of AR parameters in subblocks using Panchromatic (PAN) image gives better results for the multiresolution fusion process in remote sensing applications.