超参数
反演(地质)
反问题
算法
计算复杂性理论
反向
人工神经网络
计算机科学
斑点图案
人工智能
地质学
数学
古生物学
数学分析
几何学
构造盆地
作者
Yuchen He,Yue Zhou,Yuan Yuan,Hui Chen,Huaibin Zheng,JIanbin Liu,Yu Zhou,Chao Wang
出处
期刊:Journal of The Optical Society of America B-optical Physics
[The Optical Society]
日期:2022-10-31
卷期号:39 (11): 3100-3100
被引量:2
摘要
Ghost imaging (GI), which employs speckle patterns and bucket signals to reconstruct target images, can be regarded as a typical inverse problem. Iterative algorithms are commonly considered to solve the inverse problem in GI. However, high computational complexity and difficult hyperparameter selection are the bottlenecks. An improved inversion method for GI based on the neural network architecture TransUNet is proposed in this work, called TransUNet-GI. The main idea of this work is to utilize a neural network to avoid issues caused by conventional iterative algorithms in GI. The inversion process is unrolled and implemented on the framework of TransUNet. The demonstrations in simulation and physical experiment show that TransUNet-GI has more promising performance than other methods.
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