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


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ORAL O., ÖKTEM S. F.

IEEE Transactions on Computational Imaging, vol.10, pp.762-773, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10
  • Publication Date: 2024
  • Doi Number: 10.1109/tci.2024.3396388
  • Journal Name: IEEE Transactions on Computational Imaging
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.762-773
  • Keywords: Complex-valued reconstruction, deep priors, inverse problems, multiple-input multiple-output (MIMO), near-field microwave imaging, plug-and-play methods, radar imaging
  • Middle East Technical University Affiliated: Yes

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).