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Generative models learned from training using deep learning methods can beused as priors in inverse under-determined inverse problems, including imagingfrom sparse set of measurements. In this paper, we present a novel hierarchicaldeep-generative model MrSARP for SAR imagery that can synthesize SAR images ofa target at different resolutions jointly. MrSARP is trained in conjunctionwith a critic that scores multi resolution images jointly to decide if they arerealistic images of a target at different resolutions. We show how this deepgenerative model can be used to retrieve the high spatial resolution image fromlow resolution images of the same target. The cost function of the generator ismodified to improve its capability to retrieve the input parameters for a givenset of resolution images. We evaluate the model's performance using the threestandard error metrics used for evaluating super-resolution performance onsimulated data and compare it to upsampling and sparsity based image sharpeningapproaches.

MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution