Lq-SPB-Net: A Real-Time Deep Network for SAR Imaging and Despeckling

Large quantities of sampling data and speckle noise are two serious problems existing in synthetic aperture radar (SAR). The former puts enormous strain on data measurement, transmission, and storage. The latter deteriorates imaging quality, disturbing the subsequent processing in SAR systems. This study proposes a real-time deep network to address these issues. The proposed network is able to concurrently achieve SAR imaging and despeckling with down-sampled data. Specifically, to fit more diverse imaging regions, we suppose that the noise in the SAR imaging-despeckling observation model follows a universal complex generalized Gaussian distribution. Based on this assumption, an optimization problem with a convex Lq-norm (q > 1) fidelity term is constructed through the maximum a posteriori (MAP) estimation. We employ an L1-norm sparse constraint and a convolutional neural networks (CNNs) projection-based detail preservation constraint to further promote the imaging and speckle reduction capabilities. Then, the complex-valued split Bregman method (CV-SBM) is applied to convert the proposed problem into an equivalent sequence of sub-problems. We devise a computationally efficient solution for the fidelity term-related sub-problem, due to the specific down-sampled strategy in SAR. A substitutive cost function and a CNN structure are introduced to solve the projection-related sub-problem. Finally, the iterative steps of CV-SBM are cast into a deep network-dubbed Lq-split Bregman (SPB)-Net to yield a desirable imaging and despeckling result within a small number of iterations. Numerical experiments based on the real Radarsat-1 data validate the efficiency and feasibility of the proposed Lq-SPB-Net in real-time imaging and despeckling with down-sampled data.