CLAMP: Majorized Plug-and-Play for Coherent 3D Lidar Imaging

Tony G. Allen; David J. Rabb; Gregery T. Buzzard; Charles A. Bouman

doi:10.1109/TCI.2025.3548468

CLAMP: Majorized Plug-and-Play for Coherent 3D Lidar Imaging

Tony G. Allen
, David J. Rabb
, Gregery T. Buzzard
, Charles A. Bouman

Radar and Computational Imaging

Research output: Contribution to journal › Article › peer-review

Abstract

Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence. In this paper, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction. CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate surrogate forward model of coherent lidar. Additionally, CLAMP introduces a computationally efficient FFT-based method to account for the system's aperture to improve resolution of reconstructed images. Furthermore, we formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution. Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery.

Original language	English
Pages (from-to)	506-519
Number of pages	14
Journal	IEEE Transactions on Computational Imaging
Volume	11
DOIs	https://doi.org/10.1109/TCI.2025.3548468
State	Published - 2025

Funding

Received 19 June 2024; revised 22 November 2024 and 1 February 2025; accepted 17 February 2025. Date of publication 5 March 2025; date of current version 11 April 2025. The work of Gregery T. Buzzard was supported by NSF under Grant CCF-1763896. The work of Charles A. Bouman was supported by Showalter Trust. The associate editor coordinating the review of this article and approving it for publication was Prof. Christopher Metzler. (Corresponding author: Tony G. Allen.) Tony G. Allen was with the Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH 45433 USA, also with the Purdue University, West Lafayette, IN 47907 USA. He is now with the Oak Ridge National Laboratory, Oak Ridge, TN 37830 USA (e-mail: [email protected]).

Keywords

Coherent imaging
consensus equilibrium
deep neural networks
lidar
majorization-minimization
plug-and-play

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10.1109/TCI.2025.3548468

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title = "CLAMP: Majorized Plug-and-Play for Coherent 3D Lidar Imaging",

abstract = "Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence. In this paper, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction. CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate surrogate forward model of coherent lidar. Additionally, CLAMP introduces a computationally efficient FFT-based method to account for the system's aperture to improve resolution of reconstructed images. Furthermore, we formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution. Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery.",

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year = "2025",

doi = "10.1109/TCI.2025.3548468",

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AU - Allen, Tony G.

AU - Rabb, David J.

AU - Buzzard, Gregery T.

AU - Bouman, Charles A.

PY - 2025

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N2 - Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence. In this paper, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction. CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate surrogate forward model of coherent lidar. Additionally, CLAMP introduces a computationally efficient FFT-based method to account for the system's aperture to improve resolution of reconstructed images. Furthermore, we formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution. Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery.

AB - Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence. In this paper, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction. CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate surrogate forward model of coherent lidar. Additionally, CLAMP introduces a computationally efficient FFT-based method to account for the system's aperture to improve resolution of reconstructed images. Furthermore, we formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution. Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery.

KW - Coherent imaging

KW - consensus equilibrium

KW - deep neural networks

KW - lidar

KW - majorization-minimization

KW - plug-and-play

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JO - IEEE Transactions on Computational Imaging

JF - IEEE Transactions on Computational Imaging

ER -

CLAMP: Majorized Plug-and-Play for Coherent 3D Lidar Imaging

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