Multi-plane reconstruction is essential for realizing a holographic three-dimensional (3D) display. One fundamental issue in conventional multi-plane Gerchberg-Saxton (GS) algorithm is the inter-plane crosstalk, mainly caused by the neglect of other planes' interference in the process of amplitude replacement at each object plane. In this paper, we proposed the time-multiplexing stochastic gradient descent (TM-SGD) optimization algorithm to reduce the multi-plane reconstruction crosstalk. First, the global optimization feature of stochastic gradient descent (SGD) was utilized to reduce the inter-plane crosstalk. However, the crosstalk optimization effect would degrade as the number of object planes increases, due to the imbalance between input and output information. Thus, we further introduced the time-multiplexing strategy into both the iteration and reconstruction process of multi-plane SGD to increase input information. In TM-SGD, multiple sub-holograms are obtained through multi-loop iteration and then sequentially refreshed on spatial light modulator (SLM). The optimization condition between the holograms and the object planes converts from one-to-many to many-to-many, improving the optimization of inter-plane crosstalk. During the persistence of vision, multiple sub-hologram jointly reconstruct the crosstalk-free multi-plane images. Through simulation and experiment, we confirmed that TM-SGD could effectively reduce the inter-plane crosstalk and improve image quality.The proposed TM-SGD-based holographic display has wide applications in tomographic 3D visualization for biology, medical science, and engineering design, which need to reconstruct multiple independent tomographic images without inter-plane crosstalk.
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http://dx.doi.org/10.1364/OE.483590 | 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).
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