# ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv: https://arxiv.org/abs/2211.16855 · HTML: https://mlsanity.com/paper/2211.16855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 30 Nov 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds](https://mlsanity.com/paper/1808.10038/) (2018)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [Learned D-AMP: Principled Neural Network based Compressive Image Recovery](https://mlsanity.com/paper/1704.06625/) (2017)
- [boldsymbolγ-Net: Superresolving SAR Tomographic Inversion via Deep Learning](https://mlsanity.com/paper/2112.04211/) (2021)
- [Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding](https://mlsanity.com/paper/1709.07124/) (2017)
- [Unrolled Optimization with Deep Learning-based Priors for Phaseless Inverse Scattering Problems](https://mlsanity.com/paper/2207.07244/) (2022)
- [Fast Super-resolution 3D SAR Imaging Using an Unfolded Deep Network](https://mlsanity.com/paper/1808.08658/) (2018)

## Built on by (most cited first, 2 of 2)

- [Diffusion-Prior Split Gibbs Sampling for Synthetic Aperture Radar Imaging under Incomplete Measurements](https://mlsanity.com/paper/2512.02768/) (2025)
- [Deep Learning-based Cross-modal Reconstruction of Vehicle Target from Sparse 3D SAR Image](https://mlsanity.com/paper/2406.04158/) (2024)

## Abstract (arXiv)

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.