Deep Compressive Imaging With Meta-Learning

Recently, deep learning-based compressive synthetic aperture radar (SAR) imaging has received increasing interests. However, its performances rely heavily on the training data and could not well adapt to new observations. To solve it, in this article a robust compressive SAR imaging method is proposed under the paradigm of meta-learning. First, a Deep Skip-connected Convolutional Network (DSCN) is constructed, to learn the mapping from Low-Resolution (LR) radar returns to High-Resolution (HR) radar image of the same scene. Then a set of meta tasks are sampled from one or multiple radar systems, to guide the search of DSCN and knowledge transfer to new compressive imaging tasks. This method is non-iterative and does not require any information of radar system. Moreover, it can work for compressive imaging in both range and azimuth. Extensive experiments are taken and the results show that the proposed meta-learning-based DSCN (ML-DSCN) can achieve accurate and rapid resolution enhancement, and is superior to its counterparts in terms of reconstruction speed, robustness, and generalization.