Multi-plane crosstalk is a key issue affecting the quality of holographic three-dimensional (3D) displays. The time-multiplexing stochastic gradient descent (TM-SGD) method has been applied to solve the inter-plane crosstalk problem in multi-plane reconstruction. However, the inter-plane crosstalk increases greatly as the inter-plane interval decreases, and the optimization time increases greatly as the number of planes increases. In this paper, we propose a double-constraint stochastic gradient descent method to suppress inter-plane crosstalk in multi-plane reconstruction. In the proposed method, we use the mask to make the optimization process focus more on the signal region and improve the reconstruction quality. Meanwhile, we adopt a constraint strategy of phase regularization to reduce the phase randomness of the signal region and suppress inter-plane crosstalk. Numerical simulation and optical experiment results confirm that our method can effectively suppress the inter-plane crosstalk and improve the quality of the reconstructed planes at a lower inter-plane interval. Moreover, the optimization time of our method is almost 4 times faster than that of TM-SGD. The proposed method can contribute to the realization of tomographic 3D visualization in the biomedical field, which requires the reconstruction of multiple tomographic images without inter-plane crosstalk.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OE.499595 | DOI Listing |
Multi-plane holography has attracted increasing interest for reconstructing depth information. However, achieving multi-plane holography with high capacity and low crosstalk is always highly desired. Here, we proposed and demonstrated a novel multi-plane holography based on multiplicative noise multiplexing and temporal multiplexing.
View Article and Find Full Text PDFMulti-plane crosstalk is a key issue affecting the quality of holographic three-dimensional (3D) displays. The time-multiplexing stochastic gradient descent (TM-SGD) method has been applied to solve the inter-plane crosstalk problem in multi-plane reconstruction. However, the inter-plane crosstalk increases greatly as the inter-plane interval decreases, and the optimization time increases greatly as the number of planes increases.
View Article and Find Full Text PDFPhys Med Biol
December 2020
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, United States of America.
Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimensional nodule detection network (MD-NDNet) for automatic nodule false-positive reduction using deep convolutional neural network (DCNNs).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!