Abstract
The key point of the Super-Resolution (SR) process is the accurate registration of the low resolution images, i.e., accurate measuring of the fixed shift between them, to obtain high resolution image. Due to certain malfunction, some Egyptsat-1 images have inconsistent sub-pixel shift. Therefore, in this study we propose a methodology to use this kind of shift for reconstructing a SR image of Egyptsat-1 from its low resolution bands. It is a trade-off between the capability of catching spatial details and the sensitivity to the erratic shift existed along the image. Firstly, this inconsistent shift between the bands is transformed into reliable shift. Then a SR method based on image fusion scheme with multi-resolution decomposition is performed. The fusion process is conducted in steerable wavelet domain using normalized convolution technique. It allows the recognition of objects with size approaching its limiting spatial resolution. Results show that the proposed methods make significant spatial resolution improvements from 7.8 to 4 m. Different quantitative measures of the proposed methodology were assessed and tested with some implemented commonly used SR methods. These methods are; nonparametric bayesian, POCS, iterative-interpolation, robust and iterated back projection. The visual and quantitative evaluations verify the usefulness and effectiveness of the proposed methodology.
Highlights
Resolution algorithms do not use any High Resolution (HR) image
The remainder of the study is organized as follows: Section-1 describes the idea of the image registration, the steerable wavelet transform and the normalized convolution technique
By visual comparison of the original LR images and the reconstructed SR output, it is easy to see that the spatial resolution of the SR image is clearly better than the original ones
Summary
Resolution algorithms do not use any HR image. They only combine independent LR images from the same. The ever- resolution observations of a scene These observations increasing demand for more pixels, or higher resolution, can be captured simultaneously or at different times by a in combination with the availability of more and more single or multiple imaging devices. If they have sub-pixel computational power, has generated a large interest in shifts they contain additional information that can be super-resolution imaging (Dewalle, 2006). Applications of super-resolution both in civilian and military domain are; remote sensing, medical and Radar imaging, surveillance systems, target detection and recognition, to name a few (Borman, 2004).
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