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-sampling is constructed on the basis of typical U-net and Resnet50, making the LREC-net more suitable for the particle-field image reconstruction. Moreover, a fast feature convergence module for rapid particle-field feature acquirement is added to up-sampling process of the network to further promote the network processing efficiency. Apart from the design of LREC-net network itself, the optimization of network input dataset in SNR of images is achieved by finding a fit image reconstruction algorithm that can produce higher-SNR particle images in the computed tomography. This achievement reduces the input noise as much as possible and ensures effective particle-field feature extraction by the network.

The simulation analysis and experimental test demonstrate the effectiveness of the proposed LREC-net method, which involves the evaluations of SNR changes of the input-output images through the network, the proportion of residual artifact noise as ghost-particles (GPP) in the reconstructed images, and the valid-particle loss proportion (PLP). In contrast to the performances of U-net and Resnet50 under the same imaging conditions, all the data in SNR, GPP and PLP show the great improvement of the image quality due to the application of LREC-net method. Meanwhile, the designed LREC-net method also enhances the network running efficiency to a large extent due to the remarkable reduction of training time. Therefore, this work provides a new and effective approach for developing sparse-sampling-based fast and high-quality particle-field computed tomography.