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MAJORIZED MULTI-AGENT CONSENSUS EQUILIBRIUM FOR 3D COHERENT LIDAR IMAGING

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Název: MAJORIZED MULTI-AGENT CONSENSUS EQUILIBRIUM FOR 3D COHERENT LIDAR IMAGING
Autoři: Tony Allen
Rok vydání: 2024
Sbírka: Purdue University Graduate School: Figshare
Témata: Electrical engineering not elsewhere classified, Applied mathematics not elsewhere classified, computational imaging, inverse imaging problems, plug and play, lidar imaging, convex optimization algorithms
Popis: 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 work, 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 physics-based forward model.CLAMP introduces an FFT-based method to account for the effects of the aperture and uses majorization of the forward model for computational efficiency.We also 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.
Druh dokumentu: thesis
Jazyk: unknown
Relation: https://figshare.com/articles/thesis/MAJORIZED_MULTI-AGENT_CONSENSUS_EQUILIBRIUM_FOR_3D_COHERENT_LIDAR_IMAGING/25749858
DOI: 10.25394/pgs.25749858.v1
Dostupnost: https://doi.org/10.25394/pgs.25749858.v1
Rights: CC BY 4.0
Přístupové číslo: edsbas.C1780CA4
Databáze: BASE
Popis
Abstrakt: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 work, 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 physics-based forward model.CLAMP introduces an FFT-based method to account for the effects of the aperture and uses majorization of the forward model for computational efficiency.We also 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.
DOI:10.25394/pgs.25749858.v1