用于快照压缩成像的未训练神经网络：理论与算法
Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms
AI总结：
本文针对快照压缩成像提出基于未训练神经网络的理论框架与SCI-BDVP算法，通过优化掩码参数并利用bagged-DIP思想，在视频SCI中取得UNN方法中最优性能，且在含噪场景下超越监督方法。
摘要：
快照压缩成像（SCI）从单个二维测量中恢复高维（3D）数据立方体，使视频和高光谱成像等多种应用在采集速度和效率方面超越标准技术。本文重点关注采用未训练神经网络（UNN）（例如深度图像先验（DIP））来建模源结构的SCI恢复算法。这类基于UNN的方法具有吸引力，因为它们有可能避免针对不同源模型和不同测量场景进行计算密集型的重新训练。我们首先建立了一个理论框架，用于刻画此类基于UNN方法的性能。该理论框架一方面使我们能够优化数据调制掩码的参数，另一方面在可从单个测量中恢复的数据帧数与未训练神经网络的参数之间建立了根本性联系。我们还采用最近提出的袋装深度图像先验（bagged-DIP）思想，开发了SCI袋装深度视频先验（SCI-BDVP）算法，以解决标准UNN方案所面临的常见挑战。我们的实验结果表明，在视频SCI中，我们提出的解决方案在UNN方法中达到了最先进水平，并且在含噪测量的情况下，其性能甚至优于监督式解决方案。
英文摘要：
Snapshot compressive imaging (SCI) recovers high-dimensional (3D) data cubes from a single 2D measurement, enabling diverse applications like video and hyperspectral imaging to go beyond standard techniques in terms of acquisition speed and efficiency. In this paper, we focus on SCI recovery algorithms that employ untrained neural networks (UNNs), such as deep image prior (DIP), to model source structure. Such UNN-based methods are appealing as they have the potential of avoiding the computationally intensive retraining required for different source models and different measurement scenarios. We first develop a theoretical framework for characterizing the performance of such UNN-based methods. The theoretical framework, on the one hand, enables us to optimize the parameters of data-modulating masks, and on the other hand, provides a fundamental connection between the number of data frames that can be recovered from a single measurement to the parameters of the untrained NN. We also employ the recently proposed bagged-deep-image-prior (bagged-DIP) idea to develop SCI Bagged Deep Video Prior (SCI-BDVP) algorithms that address the common challenges faced by standard UNN solutions. Our experimental results show that in video SCI our proposed solution achieves state-of-the-art among UNN methods, and in the case of noisy measurements, it even outperforms supervised solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
