Plug-and-Play Regularization on Magnitude with Deep Priors for 3D Near-Field MIMO Imaging

• URL: http://arxiv.org/abs/2312.16024v2
• Date: Wed, 13 Mar 2024 22:27:03 GMT
• Title: Plug-and-Play Regularization on Magnitude with Deep Priors for 3D Near-Field MIMO Imaging
• Authors: Okyanus Oral, Figen S. Oktem,
• Abstract summary: Near-field radar imaging systems are used in a wide range of applications such as concealed weapon detection and medical diagnosis. We consider the problem of the three-dimensional (3D) complex-valued reflectivity by enforcing regularization on its magnitude.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Near-field radar imaging systems are used in a wide range of applications such as concealed weapon detection and medical diagnosis. In this paper, we consider the problem of reconstructing the three-dimensional (3D) complex-valued reflectivity distribution of the near-field scene by enforcing regularization on its magnitude. We solve this inverse problem by using the alternating direction method of multipliers (ADMM) framework. For this, we provide a general expression for the proximal mapping associated with such regularization functionals. This equivalently corresponds to the solution of a complex-valued denoising problem which involves regularization on the magnitude. By utilizing this expression, we develop a novel and efficient plug-and-play (PnP) reconstruction method that consists of simple update steps. Due to the success of data-adaptive deep priors in imaging, we also train a 3D deep denoiser to exploit within the developed PnP framework. The effectiveness of the developed approach is demonstrated for multiple-input multiple-output (MIMO) imaging under various compressive and noisy observation scenarios using both simulated and experimental data. The performance is also compared with the commonly used direct inversion and sparsity-based reconstruction approaches. The results demonstrate that the developed technique not only provides state-of-the-art performance for 3D real-world targets, but also enables fast computation. Our approach provides a unified general framework to effectively handle arbitrary regularization on the magnitude of a complex-valued unknown and is equally applicable to other radar image formation problems (including SAR).

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