SMLM CEP152-Complex FITS Images

<strong> Experimental Data for the Paper titled "3D Structure from SMLM images using Deep Learning"</strong> The following files comprise 19 sets of 40,000 images, each set corresponding to a different rendering sigma as described in the paper. * Extracting * To extract the files, execute the following command (under Linux): <pre><code>cat paper_data.tar.gz.* | tar xzvf -</code></pre> * Organisation * The data are grouped into 19 directories, corresponding to the sigma value they were rendered at. These values are 10, 9, 8.1, 7.29, 6.56, 5.9, 5.31, 4.78, 4.3, 3.87, 3.65, 3.28, 2.95, 2.66, 2.39, 2.15, 1.94, 1.743, 1.57, 1.41 Each directory contains 40,000 FITS files - a NASA floating point image standard. The images are single channel and un-normalised. This structure is ready to be used * Recreating the data * If you have the time and compute power, you can regenerate this data set with as many or as few images as you prefer, at any sigma level. The original experimental data is available at &lt;fill in later&gt;. To recreate the data set you need to download the CEPRender program, available on github: https://github.com/OniDaito/CEPrender - details on how to use this program are available with the code.<strong> 论文《基于深度学习的单分子定位显微镜（Single-Molecule Localization Microscopy，SMLM）图像三维结构重建》的实验数据</strong> 本次交付的文件包含19组总计40000张图像，每组均对应论文中所述的一种不同的渲染西格玛（sigma）参数。 * 解压操作 * 如需解压本数据集文件，请在Linux系统中执行如下命令： <pre><code>cat paper_data.tar.gz.* | tar xzvf -</code></pre> * 数据组织形式 * 本数据集按19个目录进行分组，每个目录对应图像渲染时使用的sigma参数值，具体参数依次为：10、9、8.1、7.29、6.56、5.9、5.31、4.78、4.3、3.87、3.65、3.28、2.95、2.66、2.39、2.15、1.94、1.743、1.57、1.41。每个目录下包含40000个FITS（灵活图像传输系统，Flexible Image Transport System）文件，该格式是美国国家航空航天局（NASA）制定的浮点图像标准。所有图像均为单通道且未经过归一化处理，该目录结构可直接投入使用。 * 数据集重建方法 * 若具备充足的时间与计算资源，您可根据自定义需求调整图像数量，并在任意sigma参数级别下重新生成本数据集。原始实验数据的获取链接将后续补充。如需重建数据集，需先下载GitHub平台上的CEPRender程序，其仓库地址为：https://github.com/OniDaito/CEPrender，程序的具体使用说明可随代码一并获取。