28-01-2016, 12:28 PM
Abstract
Super-resolution addresses the problem of estimating a high-resolution version of a low-resolution image. In this research, a learning-based approach for super-resolution is applied to remotely-sensed images. These images contain fewer sharp edges than those previously used to test the method, making the problem more difficult. The approach uses a Markov network to model the relationship between an image/scene training pair (low-resolution/high-resolution pair). Bayesian belief propagation is used to obtain the posterior probability of the scene, given a low-resolution input image. Preliminary results on Landsat-7 images show that the framework works reasonably well when given a sufficiently large training set.
Problem
Is it possible to infer a high-resolution version of an image that is first given at low resolution? Stronger approaches than simple interpolation use Bayesian techniques or regularization while assuming prior probabilities or constraints. Furthermore, these techniques are independent of both the structures present in the image and the nature of the image itself. Freeman et al. propose that statistical relationships between pairs of low-resolution and high-resolution images (image/scene pairs) can be learned. Thus, given a new image to super-resolve (of the same nature as the images in the training set), it is possible to obtain a higher-resolution image based on the statistics acquired from the training pairs. The most important features learned by the method are edges. The algorithm learns what a blurry edge should look like in its corresponding high-resolution image. Thus, the training data need to contain many sharp edges. The experiments described in Freeman et al. were done mostly on such training sets. In the present research, the specific problem of super-resolution of remotely-sensed images (with few sharp edges) is addressed.