# Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study

arXiv: https://arxiv.org/abs/2211.14990 · HTML: https://mlsanity.com/paper/2211.14990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements](https://mlsanity.com/paper/2010.15440/) (2020)
- [Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms](https://mlsanity.com/paper/2007.13290/) (2020)
- [On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty](https://mlsanity.com/paper/2005.00657/) (2020)
- [Constant-Time-Delay Interferences In Near-Field SAR: Analysis And Suppression In Image Domain](https://mlsanity.com/paper/2209.11039/) (2022)
- [Non-Common Band SAR Interferometry via Compressive Sensing](https://mlsanity.com/paper/2003.00692/) (2020)
- [Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution](https://mlsanity.com/paper/2209.10442/) (2022)

## Abstract (arXiv)

Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises, hindering the information retrieval of the target. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. Thus, they achieve limited restoration performance in terms of the target's shape, especially for complex targets. To address these issues, a preliminary study is conducted on restoration with the recent promising deep learning inverse technique in this work. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. Adhering to it, a model-based deep learning network is designed to restore the image. A simulated degraded image dataset from multiple complex target models is constructed to validate the network. All the images are formulated using the electromagnetic simulation tool. Experiments on the dataset reveal their effectiveness. Compared with current methods, superior performance is achieved regarding the target's shape and energy estimation.