Data for: Motion adaptive deblurring with single photon cameras

Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme lowlight sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work we harness these capabilities for dealing with motion blur in a passive imaging setting in low illumination conditions. Our key insight is that the data captured by a SPAD array camera can be represented as a 3D spatio-temporal tensor of photon detection events which can be integrated along arbitrary spatio-temporal trajectories with dynamically varying integration windows, depending on scene motion. We propose an algorithm that estimates pixel motion from photon timestamp data and dynamically adapts the integration windows to minimize motion blur. Our simulation results show the applicability of this algorithm to a variety of motion p..., Dataset is raw photon data captured with a Photon Force 32x32 SPAD camera under ambient illumination of a spinning fan. , , # Data for: Motion adaptive deblurring with single photon cameras

[https://doi.org/10.5061/dryad.xpnvx0kpc](https://doi.org/10.5061/dryad.xpnvx0kpc)

## Description of the data and file structure

This dataset contains the data for Motion Adaptive Deblurring with Single Photon Cameras. It contains raw data from a InGaAs 32x32 Single Photon Avalanche Diode (SPAD) array captured under ambient light. SPAD cameras are able to detect and time tag individual photons. This data comes from a frame readout SPAD which represents temporal data on a coarse and fine time scale. The coarse time scale is similar to a frame of video and is called a \"photon frame\" whereas the fine time scale represents the arrival time of photons within each photon frame. This dataset is the raw SPAD video capture of a spinning fan captured at 50k FPS.

The data represents a 100000 x 32 x 32 16-bit array. The last two dimensions represent the 32 x 32 pixels of the camera. The first dimension represents the index of a p...

单光子雪崩二极管（Single-photon avalanche diodes, SPADs）是一类快速发展的图像传感技术，具备极端低光灵敏度与皮秒级计时分辨率。这类独特的性能使其可应用于激光雷达（LiDAR）、非视距成像、荧光显微镜等需要在光子稀缺场景下开展成像的任务。本研究利用其特性，解决低光照条件下被动成像场景中的运动模糊问题。我们的核心思路是：SPAD阵列相机采集的数据可被表示为存储光子探测事件的三维时空张量，可根据场景运动情况，沿任意时空轨迹结合动态调整的积分窗口进行积分。我们提出了一种算法，可从光子时间戳数据中估计像素运动，并动态适配积分窗口以最小化运动模糊。仿真结果验证了该算法在多种运动场景下的适用性。本数据集为使用Photon Force 32×32 SPAD相机采集的原始光子数据，拍摄场景为环境光照下的旋转风扇。

# 数据集关联：单光子相机运动自适应去模糊
[https://doi.org/10.5061/dryad.xpnvx0kpc](https://doi.org/10.5061/dryad.xpnvx0kpc)

## 数据集与文件结构说明
本数据集包含“单光子相机运动自适应去模糊”研究相关的数据，包含铟镓砷（InGaAs）材质的32×32单光子雪崩二极管（SPAD）阵列在环境光照下采集的原始数据。SPAD相机可检测单个光子并为其打上时间标签。本数据来自帧读出型SPAD设备，其时间数据包含粗、细两个时间尺度：粗时间尺度类似常规视频帧，被称为“光子帧”；细时间尺度则表示每个光子帧内光子的到达时刻。本数据集为以50000帧/秒（FPS）采集的旋转风扇原始SPAD视频捕获数据。
该数据为100000×32×32的16位阵列，后两个维度对应相机的32×32像素阵列，第一维度表示单个……