Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction

TL;DR

This paper introduces AMP-Net, a deep unfolded model for compressive sampling in Quantitative Acoustic Microscopy, demonstrating improved image reconstruction quality and artifact reduction over traditional AMP methods.

Contribution

The paper presents AMP-Net, a novel deep unfolded model for AMP, tailored for QAM image reconstruction, outperforming existing methods in quality and artifact suppression.

Findings

AMP-Net achieves up to 63% PSNR improvement.

It avoids sampling pattern related artifacts.

It performs well even when trained on natural images.

Abstract

Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts.