Transformer-Based Under-sampled Single-Pixel Imaging  

作　　者：TIAN Ye FU Ying ZHANG Jun 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China [2]Yangtze Delta Region Academy of Beijing Institute of Technology,Jiaxing 314019,China [3]School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China [4]Advanced Research Institute of Multidisciplinary Science,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Chinese Journal of Electronics》2023年第5期1151-1159,共9页电子学报（英文版）

基　　金：supported by the National Key R&D Program of China(2022YFC3300704);the National Natural Science Foundation of China(62171038,61827901,62088101).

摘　　要：Single-pixel imaging,as an innovative imaging technique,has attracted much attention during the last decades.However,it is still a challenging task for single-pixel imaging to reconstruct high-quality images with fewer measurements.Recently,deep learning techniques have shown great potential in single-pixel imaging especially for under-sampling cases.Despite outperforming traditional model-based methods,the existing deep learning-based methods usually utilize fully convolutional networks to model the imaging process which have limitations in long-range dependencies capturing,leading to limited reconstruction performance.In this paper,we present a transformer-based single-pixel imaging method to realize high-quality image reconstruction in under-sampled situation.By taking advantage of self-attention mechanism,the proposed method is good at modeling the imaging process and directly reconstructs high-quality images from the measured one-dimensional light intensity sequence.Numerical simulations and real optical experiments demonstrate that the proposed method outperforms the state-of-the-art single-pixel imaging methods in terms of reconstruction performance and noise robustness.

关 键 词：Computational imaging Single-pixel imaging Vision transformer Under-sampled ratio 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]