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

TL;DR

This paper introduces CLAMP, a novel method for 3D coherent lidar imaging that combines neural network denoising with an aperture-aware model, ensuring convergence and significantly enhancing image resolution and quality.

Contribution

It presents a new PnP framework with a theoretical convergence guarantee and an FFT-based aperture correction for improved lidar image reconstruction.

Findings

Achieves high-resolution, speckle-free 3D images from synthetic and real data.

Provides a convergence proof for majorization-minimization in consensus optimization.

Demonstrates superior resolution compared to existing methods.

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.