Radar/SAR Image Resolution Enhancement via Unifying Descriptive Experiment Design Regularization and Wavelet-Domain Processing

Modern approaches for resolution enhancement (RE) and superresolution (SR) of coherent remote sensing (RS) imagery suggest to exploit the sparsity of the desired image representations in some appropriately chosen overcomplete dictionaries and treat the related RE/SR imaging inverse problems in descriptive settings imposing some structured regularization constraints. However, such approaches are not properly adapted to the SR recovery of the speckle-corrupted low resolution (LR) coherent radar imagery with preservation of salient image features. In this letter, we address a new multistage iterative SR technique for feature-enhanced radar/fractional synthetic aperture radar computational imaging. First, the despeckled high-resolution image is recovered from the LR speckle-corrupted radar image applying the descriptive-experiment-design-regularization-based reconstructive processing. Next, the multistage RE is consequently performed in each nested refined SR frame via the iterative reconstruction of the upscaled radar images, followed by the discrete-wavelet-transform-based sparsity-promoting denoising with guaranteed consistency preservation in each resolution frame.