TransUNet-based inversion method for ghost imaging
Yuchen He, Yue Zhou, Yuan Yuan, Hui Chen, Huaibin Zheng, Jianbin Liu, Yu Zhou, Zhuo Xu
Abstract
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.