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

TL;DR

This paper introduces MrSARP, a hierarchical deep generative model for SAR image super-resolution that synthesizes multi-resolution images and retrieves high-resolution images from low-resolution inputs.

Contribution

The paper presents a novel hierarchical deep generative model, MrSARP, trained with a critic for SAR image super-resolution across multiple resolutions, improving upon existing methods.

Findings

MrSARP outperforms traditional upsampling methods.

The model effectively retrieves high-resolution images from low-resolution inputs.

Evaluation shows improved accuracy using standard super-resolution metrics.

Abstract

Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using the three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image sharpening approaches.