用于定量声学显微镜图像重建的深度展开近似消息传递
Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction
AI总结：
本文提出将深度展开模型AMP-Net用于定量声学显微镜参数图谱的压缩感知重建，相比传统方法显著提升了PSNR并避免了采样伪影。
摘要：
定量声学显微镜（QAM）是一种利用高频超声波生成微观尺度下生物组织声学和力学特性定量二维（2D）图谱的成像技术。提高QAM的频率可获得更精细的分辨率，但代价是采集时间和数据存储成本增加。压缩感知（CS）方法已被用于从减少的样本集中生成QAM图像，最近的最先进技术采用了近似消息传递（AMP）方法。在本文中，我们研究了AMP-Net（一种用于AMP的深度展开模型）在QAM参数图谱的CS重建中的应用。结果表明，即使在基于自然图像训练的默认配置下，AMP-Net也能提供卓越的重建性能（PSNR最高提升63%），同时避免了采样模式相关伪影的出现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
