Digital Earth rendering applications, such as Google Earth and World Wind, allow us to explore real information of the
Earth surface. To show the diverse details of the Earth surface, it requires high resolution satellite images. In some cases
obtaining high resolution satellite images cost too much and sometimes there are even no such images for the interesting
sites. In this paper, we present a method using example-based super-resolution techniques combined with image
analogies framework to improve the visual quality of satellite images. Detailed high resolution and low resolution
satellite images of the same site are regarded as example pairs to form a super-resolution filter. The filter effectively
improves resolution of low-resolution satellite images. Moreover, it preserves the coherence of the images and improves
the performance of the Digital Earth applications as well. The proposed method has been tested on the World Wind,
experiment results show the effectiveness of our method.
The goal of single-frame Super-Resolution is to improve the spatial resolution of a given low-resolution image. However,
it is ill-posed. Regularization which can be interpreted as the way of finding the prior distribution of images plays a
crucial role in solving this problem. Example-based approach is one of the well-established regularization techniques for
image process based on the prior information stored in the database, which is also used for image Super-Resolution
reconstruction. This paper previews the Exampled-based Super-Resolution approach which is based on Freeman's work.
We show how the example images to be used to generate training set, describing the Super-Resolution synthesis
processing based on the training set, with the plausible experiment results on the single-frame image scale-up. Finally,
the related problems and future challenges in this field are also mentioned.