In this paper, a unified superresolution framework, which simultaneously integrates image reconstruction and image registration, is proposed for multiangle-multispectral remote sensing images. With the strong correlation of different angle images, the low-rank structure of multiangle image is explored by lexicographically ordering the 3-D data cube into a 2-D matrix. Based on this property, the observed images is decomposed to a background image and a foreground image. Considering the high relationship between each band of the multispectral image, a multichannel total variation (MTV) constraint was applied on the target high resolution background image. Therefore, a robust superresolution model of multiangle-multispectral remote sensing image based on rank minimization (RSRRM) is proposed to register the low resolution multi-angle images and reconstruct high-resolution image. This method can achieve image registration and superresolution at the same time. Several experiments are conducted to illustrate the performance of the method.
['Hang Chen', 'Hongyan Zhang', 'Liangpei Zhang']
Robust superresolution of multiangle-multispectral remote sensing images based on rank minimization