Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.In general,approximate solutions can ...Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.In general,approximate solutions can be obtained by iterative optimization methods.In the current work,a practical particle reconstruction method based on a convolutional neural network(CNN)with geometry-informed features is proposed.The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique(ART)based methods.Compared with available ART-based algorithms,the novel technique makes significant improvements in terms of reconstruction quality,robustness to noise,and at least an order of magnitude faster in the offline stage.展开更多
基金supported by the National Key R&D Program of China(No.2020YFA040070)the National Natural Science Foundation of China(grant No.11721202)the Program of State Key Laboratory of Marine Equipment(No.SKLMEA-K201910)。
文摘Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.In general,approximate solutions can be obtained by iterative optimization methods.In the current work,a practical particle reconstruction method based on a convolutional neural network(CNN)with geometry-informed features is proposed.The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique(ART)based methods.Compared with available ART-based algorithms,the novel technique makes significant improvements in terms of reconstruction quality,robustness to noise,and at least an order of magnitude faster in the offline stage.
