摘要
由于流场中的微粒分布状态能够充分表征场的特性,因此通过稀疏采样实现快速和高质量的粒子场成像始终是实验流体力学等领域高度期盼的.近年来,随着深度学习应用于粒子计算层析成像,如何提高神经网络的处理效率和质量,以消除稀疏采样所致的粒子层析图像伪影噪声仍然是一个挑战性课题.为解决这一问题,本文提出了一种新的抑制粒子场层析成像伪影噪声和提高网络效率的神经网络方法.该方法在设计上包含了轻量化双残差下采样图像压缩和特征识别提取、快速特征收敛的上采样图像恢复,以及基于经典计算层析成像算法的优化信噪比网络输入样本集构建.对整个成像系统的模拟分析和实验测试表明,相比于经典的U-net和Resnet50网络方法,本文提出的方法不仅在输出/输入的粒子图像信噪比、重建像的残余伪影噪声(即鬼粒子占比)和有效粒子损失比方面获得了极大的改进,而且也显著提高了网络的训练效率.这对发展基于稀疏采样的快速和高质量粒子场计算层析成像提供了一个新的有效方法.
The realization of fast and high-quality three-dimensional particle-field image characterization is always highly desired in the areas,such as experimental fluid mechanics and biomedicine,for the micro-particle distribution status in a flow-field can characterize the field properties well.In the particle-field image reconstruction and characterization,a wildly used approach at present is the computed tomography.The great advantage of the computed tomography for particle-field image reconstruction lies in the fact that the full particle spatial distribution can be obtained and presented due to multi-angle sampling.Recently,with the development and application of deep learning technique in computed tomography,the image quality has been greatly improved by the powerful learning ability of a deep learning network.In addition,the deep learning application also makes it possible to speed up the computed tomographic imaging process from sparse-sampling due to the ability of the network to strongly extract image feature.However,sparse-sampling will lead to insufficient acquirement of the object information during sampling for the computed tomography.Therefore,a sort of artifact noise will emerge and be accompanied with the reconstructed images,and thus severely affecting the image quality.As there is no universal network approach that can be applied to all types of objects in the suppression of artifact noise,it is still a challenge in removing the sparse-sampling-induced artifact noise in the computed tomography now.Therefore,we propose and develop a specific lightweight residual and enhanced convergence neural network(LREC-net)approach for suppressing the artifact noise in the particle-field computed tomography.In this method,the network input dataset is also optimized in signal-to-noise ratio(SNR)in order to reduce the input noise and ensure the effective particle image feature extraction of the network in the imaging process.In the design of LREC-net architecture,a five-layer lightweight and dual-residual down-sampli
作者
施岳
欧攀
郑明
邰含旭
王玉红
段若楠
吴坚
Shi Yue;Ou Pan;Zheng Ming;Tai Han-Xu;Wang Yu-Hong;Duan Ruo-Nan;Wu Jian(School of Physics,Beihang University,Beijing 100191,China;School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China)
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2024年第10期162-174,共13页
Acta Physica Sinica
基金
国家自然科学基金(批准号:61874117)资助的课题。
关键词
粒子场成像
计算层析成像
深度学习
噪声抑制
particle field imaging
computed tomography
deep learning
noise suppression
