  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 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.
