Ge Youran, Qu Gangrong, Huang Yuhao, Liu Duo
Appl Opt. 2024 May 20;63(15):4109-4117. doi: 10.1364/AO.510414.
Coded aperture compressive temporal imaging (CACTI) utilizes compressive sensing (CS) theory to compress three dimensional (3D) signals into 2D measurements for sampling in a single snapshot measurement, which in turn acquires high-dimensional (HD) visual signals. To solve the problems of low quality and slow runtime often encountered in reconstruction, deep learning has become the mainstream for signal reconstruction and has shown superior performance. Currently, however, impressive networks are typically supervised networks with large-sized models and require vast training sets that can be difficult to obtain or expensive. This limits their application in real optical imaging systems. In this paper, we propose a lightweight reconstruction network that recovers HD signals only from compressed measurements with noise and design a block consisting of convolution to extract and fuse local and global features, stacking multiple features to form a lightweight architecture. In addition, we also obtain unsupervised loss functions based on the geometric characteristics of the signal to guarantee the powerful generalization capability of the network in order to approximate the reconstruction process of real optical systems. Experimental results show that our proposed network significantly reduces the model size and not only has high performance in recovering dynamic scenes, but the unsupervised video reconstruction network can approximate its supervised version in terms of reconstruction performance.
编码孔径压缩时域成像（CACTI）利用压缩感知（CS）理论将三维（3D）信号压缩为二维测量值，以便在单次快照测量中进行采样，进而获取高维（HD）视觉信号。为了解决重建过程中经常遇到的质量低和运行时间长的问题，深度学习已成为信号重建的主流方法，并展现出卓越的性能。然而，目前令人印象深刻的网络通常是具有大型模型的监督网络，需要大量难以获取或成本高昂的训练集。这限制了它们在实际光学成像系统中的应用。在本文中，我们提出了一种轻量级重建网络，该网络仅从有噪声的压缩测量值中恢复高维信号，并设计了一个由卷积组成的模块来提取和融合局部与全局特征，堆叠多个特征以形成轻量级架构。此外，我们还基于信号的几何特征获得了无监督损失函数，以确保网络具有强大的泛化能力，从而近似实际光学系统的重建过程。实验结果表明，我们提出的网络显著减小了模型大小，不仅在恢复动态场景方面具有高性能，而且无监督视频重建网络在重建性能上可以近似其监督版本。
