Synthetic aperture radar imaging from sub-Nyquist samples by using deep priors of image

As an active microwave imaging technology, synthetic aperture radar (SAR) has extensive application prospects in remote sensing, surveying, and mapping. How to reconstruct SAR images from the subsampled echo data has always been a challenging issue. Compressed sensing technology attempts to use the sparse characteristics of SAR images to recover the SAR images from the compressed samples. However, in practical applications, SAR images do not always meet the characteristic of sparsity. In this paper, we propose an imaging method from sub-Nyquist sampled data based on deep priors of SAR images. First, we use a generative flow network to model the deep prior information of the images based on the existing SAR image datasets. Then, the pre-trained network modeling deep prior information of images is embedded in a typical compressed sensing method, i.e., the Iterative Shrinkage-Thresholding Algorithm (ISTA), to replace the sparse shrinkage function of it. Meanwhile, in order to improve the accuracy and convergence speed of reconstruction, we used the subsampled echo data to fine-tune the iterative parameters of the embedded ISTA method. The results of the experiments show that through the use of the deep prior information of the images, this method can accurately reconstruct non-sparse SAR images from the subsampled echoes, even if only a few echo samples are available.