Gao Wei, Yan Qiu-Rong, Zhou Hui-Lin, Yang Sheng-Tao, Fang Zhe-Yu, Wang Yu-Hao
Opt Express. 2021 Feb 15;29(4):5552-5566. doi: 10.1364/OE.413925.
Single photon counting compressive imaging, a combination of single-pixel-imaging and single-photon-counting technology, is provided with low cost and ultra-high sensitivity. However, it requires a long imaging time when applying traditional compressed sensing (CS) reconstruction algorithms. A deep-learning-based compressed reconstruction network refrains iterative computation while achieving efficient reconstruction. This paper proposes a compressed reconstruction network (OGTM) based on a generative model, adding sampling sub-network to achieve joint-optimization of sampling and generation for better reconstruction. To avoid the slow convergence caused by alternating training, initial weights of the sampling and generation sub-network are transferred from an autoencoder. The results indicate that the convergence speed and imaging quality are significantly improved. The OGTM validated on a single-photon compressive imaging system performs imaging experiments on specific and generalized targets. For specific targets, the results demonstrate that OGTM can quickly generate images from few measurements, and its reconstruction is better than the existing compressed sensing recovery algorithms, compensating defects of the generative models in compressed sensing.
单光子计数压缩成像结合了单像素成像和单光子计数技术，具有低成本和超高灵敏度的特点。然而，应用传统压缩感知（CS）重建算法时需要较长的成像时间。基于深度学习的压缩重建网络在实现高效重建的同时避免了迭代计算。本文提出了一种基于生成模型的压缩重建网络（OGTM），增加采样子网以实现采样和生成的联合优化，从而获得更好的重建效果。为避免交替训练导致的收敛速度缓慢，采样子网和生成子网的初始权重从自动编码器转移。结果表明，收敛速度和成像质量得到了显著提高。在单光子压缩成像系统上验证的OGTM对特定和通用目标进行了成像实验。对于特定目标，结果表明OGTM可以从少量测量中快速生成图像，其重建效果优于现有的压缩感知恢复算法，弥补了压缩感知中生成模型的缺陷。
