methods: 本方法基于tensor decompositions，并通过alternating direction method of multipliers（ADMM）算法实现了约束杂合约束（abundance sum-to-one）。此外，本研究还提出了在MultiHU-TD中 incorporate mathematical morphology和 neighboorhood patches的方法。
results: 实验表明，MultiHU-TD方法可以提供可解释的模型和分析结果，并且可以应用于实际高spectral图像分析 task。Python和MATLAB实现在GitHub上可用。Abstract
Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework. Tensor models such as canonical polyadic decomposition allow for this kind of unmixing but lack a general framework and interpretability of the results. In this article, we propose an interpretable methodological framework for low-rank multifeature hyperspectral unmixing based on tensor decomposition (MultiHU-TD) that incorporates the abundance sum-to-one constraint in the alternating optimization alternating direction method of multipliers (ADMM) algorithm and provide in-depth mathematical, physical, and graphical interpretation and connections with the extended linear mixing model. As additional features, we propose to incorporate mathematical morphology and reframe a previous work on neighborhood patches within MultiHU-TD. Experiments on real HSIs showcase the interpretability of the model and the analysis of the results. Python and MATLAB implementations are made available on GitHub.
摘要
hyperspectral unmixing可以将杂合像 Represented as a set of pure materials weighted by their abundances. spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework. Tensor models such as canonical polyadic decomposition allow for this kind of unmixing but lack a general framework and interpretability of the results.在本文中，我们提出了一种可解释的方法oloyg framework for low-rank multifeature hyperspectral unmixing based on tensor decomposition (MultiHU-TD)，该方法包括了积分权重的权重积分法ADMM算法中的积分总等于一个约束，并提供了深入的数学、物理和图形解释，以及与扩展线性混合模型的连接。此外，我们还提出了在MultiHU-TD中包含数学形态和 reformulate a previous work on neighborhood patches的方法。实验表明，该模型具有可解释性和分析结果的优点。Python和MATLAB实现在GitHub上提供。
Role of Spatial Coherence in Diffractive Optical Neural Networks
paper_authors: Matthew J. Filipovich, Aleksei Malyshev, A. I. Lvovsky
for: 这 paper 是用于研究Diffractive optical neural networks (DONNs) 的应用于计算机视觉任务中的快速和能效的信号处理方法。
methods: 这 paper 使用了数值方法来模拟 DONNs 在不同空间干涉度下的运行，并研究了这些方法的计算复杂度。
results: 研究发现，在完全无干涉照明下，DONN 的性能不能超过线性模型。 authors 还通过使用不同的空间干涉度来训练和评估 DONNs 在 MNIST 数据集上的表现。Abstract
Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. However, previous experimental demonstrations of DONNs have only been performed using coherent light, which is not present in the natural world. Here, we study the role of spatial optical coherence in DONN operation. We propose a numerical approach to efficiently simulate DONNs under input illumination with arbitrary spatial coherence and discuss the corresponding computational complexity using coherent, partially coherent, and incoherent light. We also investigate the expressive power of DONNs and examine how coherence affects their performance. In particular, we show that under fully incoherent illumination, the DONN performance cannot surpass that of a linear model. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits using light with varying spatial coherence.
摘要
干扰性光学神经网络（DONNs）已经出现为光学硬件平台，用于机器学习任务，特别是计算机视觉领域的快速和能效处理。然而，之前的实验性证明只使用了同步光，这不是自然界中存在的。在这里，我们研究了DONN操作中的空间光干扰的作用。我们提出了一种数字方法，用于高效地模拟DONNs，并对输入干扰的任意空间干扰进行计算复杂性分析。此外，我们还 investigate了DONN表达力的问题，并证明在完全不干扰的照明下，DONN性能不能超过线性模型。为 Proof of concept，我们使用了MNIST数据集的手写数字进行训练和评估，并使用不同的空间干扰来评估DONN的性能。
results: 实验表明，该方法可以在不牺牲空间分辨率的情况下，重建多达50个 spectral channel 图像。这种方法在成本、体积和时间频率等方面具有优势，可能用于开发一种可靠、高效的多спектраль摄像头。Abstract
This paper presents a Multispectral imaging (MSI) approach that combines the use of a diffractive optical element, and a deep learning algorithm for spectral reconstruction. Traditional MSI techniques often face challenges such as high costs, compromised spatial or spectral resolution, or prolonged acquisition times. In contrast, our methodology uses a single diffractive lens, a grayscale sensor, and an optical motor to capture the Multispectral image without sacrificing spatial resolution, however with some temporal domain redundancy. Through an experimental demonstration, we show how we can reconstruct up to 50 spectral channel images using diffraction physical theory and a UNet-based deep learning algorithm. This approach holds promise for a cost-effective, compact MSI camera that could be feasibly integrated into mobile devices.
摘要