Sub-Image Blocks Based Joint Sparse Reconstruction Algorithm for Multi-Pass SAR Images Feature Enhancement

With the development of multi-pass synthetic aperture radar (SAR) imaging, feature enhancement with multiple SAR images has become an important research topic due to its distinct advantage of redundant scene information. The existing SAR image feature enhancement algorithms mainly focus on single SAR image processing by means of ℓq (0 < q ≤ 1) norm regularization methods. In this paper, for the multi-pass SAR images, we first separate the entire SAR image into sub-image blocks. Then, a new discriminant criterion measure is proposed to divide all sub-image blocks into two categories; that is, existing and non-existing targets. Finally, for the sub-image blocks of existing targets, the ℓ2,1-norm minimization approach is introduced to achieve the purpose of feature enhancement. In addition, two dimensional fast iterative shrinkage thresholding algorithm (2D-FISTA) is utilized to increase the computational efficiency. Experimental results are illustrated to validate that the proposed method can improve SAR image performance significantly in terms of denoising and sidelobes suppression while maintaining higher computational efficiency.