SAR super resolution via multi-dictionary

This paper presents a novel approach for super-resolution (SR) reconstruction in Synthetic Aperture Radar (SAR), based on multi-dictionary. In comparison with conventional SR via sparse representation, the algorithm combines the classification with sparse representation. After classifying the training image, we jointly train the low and high resolution dictionaries for each class. And then, the image patches are reconstructed according to different dictionaries, which are chosen in conformity with the class of the image patches. The effectiveness of this method is demonstrated on Terra-SAR datasets.