Convallaria Photon Counting Dataset (Conv-PC)

Data was recorded from a Convallaria majalis rhizome section sample slide at a Leica TCS SP8 TPE DIVE with FALCON and the HC PL IRAPO 25x dipping objective. We used 850 nm excitation at 1% laser power, HyD-RLD detector, emission range of 600 – 650 nm, pixel size 0.6 x 0.6 µm, an 8 MHz resonant scanner and 4x averaging.We imaged 5 fields of view (FOVs), each containing 512x512x2048 voxels (xyt). Four FOVs were used as training data for supervised denoising, and the fifth FOV was used as test data.We used a time binning of 4, resulting in datasets of 512x512x512 voxels, and named these low-SNR raw data 'trainingData.tif' and 'testData.tif' respectively.Furthermore, we summed these 512 frames to produce the high SNR ('ground truth') version and named it 'trainingDataGT.tif' and 'testDataGT.tif'.Each frame of the raw data contains a significant amount of image noise. All voxel values correspond to photon counts.本数据集采集自使用徕卡（Leica）TCS SP8 TPE DIVE激光共聚焦显微镜搭配FALCON探测器与HC PL IRAPO 25×浸液物镜拍摄的铃兰（Convallaria majalis）根状茎切片玻片样本。实验采用850 nm波长激发，激光功率设置为1%，搭载HyD-RLD探测器，采集600–650 nm波段的发射信号，像素尺寸为0.6×0.6 μm，使用8 MHz共振扫描仪并执行4次平均成像。本研究共采集5个视场（Field of View，FOV），每个视场包含512×512×2048个体素，对应xyt三维维度。其中4个视场作为监督去噪任务的训练数据，剩余1个视场作为测试数据。我们采用4倍时间分箱处理，得到体素尺寸为512×512×512的数据集，并将该低信噪比原始数据分别命名为"trainingData.tif"与"testData.tif"。进一步地，我们对512帧原始数据进行求和操作，以生成高信噪比的真值（ground truth）版本数据集，并分别命名为"trainingDataGT.tif"与"testDataGT.tif"。所有原始数据的每一帧均含有大量图像噪声，所有体素值均对应光子计数结果。