Single Image Super-Resolution for Optical Satellite Scenes Using Deep Deconvolutional Network

In this paper, we deal with the problem of super-resolution (SR) imaging and propose a deep deconvolutional network based model for the same. In principle, the SR problem considers the construction of the high-resolution (HR) version of a scene given a number of so-called low-level image instances of the respective scene. Moreover, if there is a single low-resolution (LR) image available, the problem becomes even difficult and ill-posed. We deal with such a scenario and show how the popular deconvolutional network can effectively reconstruct the HR image by learning the functional mapping at the patch level. We evaluate the proposed model on a number of optical remote sensing (RS) images obtained from the UC-Merced dataset. Experimental results suggest that the proposed model consistently outperforms the existing deep and shallow models for single image SR for the RS images.