Efficient 3D Image Reconstruction of Airborne TomoSAR Based on Back Projection and Improved Adaptive ISTA

Abstract

Airborne SAR tomography (TomoSAR) 3D image reconstruction can be realized with combination of 2D imaging algorithms and compressed sensing (CS) algorithms. However, most typical CS algorithms can not achieve a balance between algorithm efficiency and 3D reconstruction accuracy. Due to difficulties in flight path control of airborne SAR, it is hard to realize registration of SAR images with frequency-domain imaging algorithms because of time-varying baseline. To address these problems, an efficient 3D image reconstruction method for airborne TomoSAR based on back projection (BP) algorithm and improved adaptive iterative shrinkage-thresholding algorithm (IA-ISTA) is proposed. First, 2D images are achieved with BP algorithm on the ground plane. After registration of SAR images, 3D image reconstruction results in the elevation direction are realized with IA-ISTA. Selection criterion of IA-ISTA parameters are given in this paper. At last, final 3D image reconstruction results are achieved after geometrical transformation based on geometric relationship. Both simulated data and measured data of a P-band airborne TomoSAR system are used. 3D image reconstruction results show that the proposed method outperforms traditional methods regarding efficiency and accuracy, which proves the validity and practicality of the proposed method.INDEX TERMS Airborne SAR tomography (TomoSAR), 3D image reconstruction, compressed sensing (CS), iterative shrinkage-thresholding algorithm (ISTA), back projection (BP) algorithm.

The Effect of CT Imaging Technology in the Diagnosis of Thoracic and Cardiac Surgery Diseases

“…Therefore, the traditional “twice-holding” scanning method greatly reduces the requirement for long-term breath-holding scanning of patients and has the maximum application value. Due to individual differences, the quality of coronary subtraction images obtained from “two breath hold” scans is affected by many factors [ 8 ]. Cardillo and Emi found that the quality of CT pulmonary vein imaging depends on the best enhancement of the left atrium and proximal pulmonary vein, while the enhancement of the pulmonary artery is not obvious; that is, there is a significant difference in enhancement between the pulmonary vein and the pulmonary artery, which is helpful to distinguish the pulmonary artery from the pulmonary vein [ 9 ].…”

Superiorization versus regularization: A comparison of algorithms for solving image reconstruction problems with applications in computed tomography

“…ISTA, FISTA, and MFISTA, with each modified to solve a model with the TV as the penalty term or the L 1 norm as the penalty term. The most basic of the three, ISTA, 13,21 takes on iterations of the form…”

“…The most basic of the three, ISTA, 13,21 takes on iterations of the form $$\begin{equation} x_{k+1} = \text{prox}_{\frac{1}{\ell } g} {\left(x_k - \frac{1}{\ell }A^T(Ax_k - b) \right)}, \end{equation}$$where $$\ell$$ is an upper bound on the Lipschitz constant of $$\nabla f$$ and recalling that $$g(x) = \lambda R(x)$$. The proximal operator for some function $$h$$ is defined as 22 $$\begin{equation} \text{prox}_h(x) = \operatornamewithlimits{arg\,min}_{u} {\left(h(u) + \frac{1}{2}\Vert u - x\Vert ^2 \right)}.$$…”

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

“…Aiming at efficient TomoSAR imaging, we focus on a holistic integration of advanced deep unfolding techinques with sparse signal processing algorithm. Along this line, a novel sparse unfolding network, called analytic learned ISTA (ALISTA), has been recently proposed as the unfolded network of the ISTA algorithm [12], which is a popular sparse reconstruction algorithm and has been broadly utilized in TomoSAR problems [13]. In ALISTA, the key parameters of ISTA are learned from training data via deep learning.…”