Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
564384 | Digital Signal Processing | 2016 | 9 Pages |
This paper investigates an efficient compressed sensing (CS) approach that can be used to reconstruct 2-D millimeter-wave synthetic aperture radar (SAR) images from under-sampled measurements. This approach minimizes a linear combination of four terms corresponding to a least squares data fitting, ℓ1ℓ1 norm regularization, total variation (TV) and a bounding operator. Although the strong convergence of this approach cannot be guaranteed, this approach always converges to a stable structural similarity (SSIM) value with a combination of a parallel operator splitting structure and a FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) updating stage. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and computation complexity.