论文标题
AutodeConj：3D光场反卷积的GPU加速ImageJ插件，具有最佳的迭代编号预测
AutoDeconJ: a GPU accelerated ImageJ plugin for 3D light field deconvolution with optimal iteration numbers predicting
论文作者
论文摘要
光场显微镜是高速3D荧光成像的紧凑型解决方案。通常，我们需要对捕获的原始数据进行3D反卷积。尽管有深层的神经网络方法可以加速重建过程，但该模型并非普遍适用于所有系统参数。在这里，我们开发了AutodeConj，这是一个GPU加速的ImageJ插件，可更快，准确地对光场显微镜数据进行精确的反向卷积。我们进一步为反卷积过程提出了图像质量指标，以自动确定具有更高重建精度和更少伪像的最佳迭代次数
Light field microscopy is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU accelerated ImageJ plugin for 4.4x faster and accurate deconvolution of light field microscopy data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifacts
