论文标题
视频快照压缩成像的深度平衡模型
Deep Equilibrium Models for Video Snapshot Compressive Imaging
论文作者
论文摘要
快照压缩成像（SCI）系统有效捕获高维（HD）数据的能力导致了一个反问题，该问题包括从压缩和嘈杂的测量中恢复HD信号。尽管重建算法在最新的深度学习进展中迅速解决，但准确和稳定的恢复的基本问题仍然存在。为此，我们提出了视频SCI的深平衡模型（DEQ），以理论上的声音方式融合数据驱动的正则化和稳定的收敛性。每个平衡模型都隐含地学习非专业运算符，并分析计算固定点，从而实现无限制的迭代步骤和无限网络深度，仅在训练和测试中需要恒定的内存要求。具体而言，我们演示了如何将DEQ应用于视频SCI重建的两个现有模型：经过重复的神经网络（RNN）和插件（PNP）算法。在各种数据集和实际数据上，我们结果的定量和定性评估都证明了我们提出的方法的有效性和稳定性。代码和型号可在以下网址提供：https：//github.com/indigopurple/deqsci。
The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data has led to an inverse problem, which consists of recovering the HD signal from the compressed and noisy measurement. While reconstruction algorithms grow fast to solve it with the recent advances of deep learning, the fundamental issue of accurate and stable recovery remains. To this end, we propose deep equilibrium models (DEQ) for video SCI, fusing data-driven regularization and stable convergence in a theoretically sound manner. Each equilibrium model implicitly learns a nonexpansive operator and analytically computes the fixed point, thus enabling unlimited iterative steps and infinite network depth with only a constant memory requirement in training and testing. Specifically, we demonstrate how DEQ can be applied to two existing models for video SCI reconstruction: recurrent neural networks (RNN) and Plug-and-Play (PnP) algorithms. On a variety of datasets and real data, both quantitative and qualitative evaluations of our results demonstrate the effectiveness and stability of our proposed method. The code and models are available at: https://github.com/IndigoPurple/DEQSCI .
