Towards super-resolution recovery of microwave sensor imagery: A unified descriptive experiment design regularization framework with projections onto nested resolution frames

We address a new approach for enhanced microwave remote sensing (RS) imaging via performing the imaging system kernel point spread function (PSF) operator refinement-based multi-scale iterative reconstructive (MSIR) image post-processing, as required for emerging feature enhanced RS missions. The high-resolution (HR) image is first reconstructed from the initial low-resolution (LR) image employing the statistically optimal minimum risk inspired descriptive experiment design regularization (DEDR) framework. Next, to approach the overall super-resolution (SR) imaging performances we incorporate into the DEDR method the additional postprocessing stage aimed at filling in the null space of the HR imaging system PSF operator via performing the corresponding projections onto the nested refined resolution greed frames. We feature the differences between the proposed DEDR-MSIR resolution refinement approach and the most competing celebrated Papoullis-Gerchberg SR method adapted for the feature enhanced RS imaging and demonstrate the advantages of the unified DEDR-MSIR approach for SR image recovery.