构建并开源2.5万张多镜头无透镜成像数据集，支持机器学习重建研究。
Scalable dataset acquisition for data-driven lensless imaging
- 多镜头并行采集，相同成像条件下同步获取数据
- 提供25,000张配对计算真值的图像数据
- 开源硬件方案与同步代码，支持复现和系统设计
基于数据的无透镜成像技术（如基于机器学习的重建算法）需要大规模数据集。本文提出一种数据采集流程，可并行捕获多个无透镜成像系统的图像，在相同成像条件下，并与计算生成的真值进行配对。我们公开了一个包含25,000张图像的数据集，涵盖两个无透镜成像设备，提供可复现的硬件配置及开源相机同步代码。该实验数据集可推动无透镜成像中的数据驱动研究，包括基于机器学习的重建算法与端到端系统设计。
原文摘要 · Abstract (English)
Data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms, require large datasets. In this work, we introduce a data acquisition pipeline that can capture from multiple lensless imaging systems in parallel, under the same imaging conditions, and paired with computational ground truth registration. We provide an open-access 25,000 image dataset with two lensless imagers, a reproducible hardware setup, and open-source camera synchronization code. Experimental datasets from our system can enable data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms and end-to-end system design.
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