SAR images super resolution via cartoon texture image decomposition and jointly optimized regressors
This paper presents a novel approach to enhance the spatial resolution of Synthetic Aperture Radar(SAR) images. SAR images super resolution(SR) reconstruction is challenging since SAR images has more complex structures. Inspired by the recent advance on natural image SR techniques, we propose a joint learning based strategy[1] combined with the characteristics of SAR image, to reconstruct HR SAR images from LR SAR images. Our method has ability to handle the complicated structures of SAR images. Besides, SAR images are decomposed into cartoon components and texture components and processed respectively. The purpose of decomposing strategy is to reduce the influence of speckle noise of SAR images. The experimental results and comparative analyses verify the effectiveness of this algorithm.
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)