Processed InShaPe dataset: Large-scale multi-shape phase retrieval dataset for PBF-LB/M laser beam shaping SLM phase mask estimation.

Copyright claim: This dataset, the "Processed InShaPe dataset for FourierGSNet" is modified from the original InShaPe dataset [1] published by the publication "Deep learning based phase retrieval with complex beam shapes for beam shape correction" [2] under the terms and conditions of CC BY 4.0 LicenseModifications from the original InShaPe dataset:This dataset and the original InShaPe dataset are both paired dataset. The pairs in the original InShaPe dataset are: 12 Zernike coefficients - 3 intensity images; but the pairs in this dataset are modified from the original corresponding sample in InShaPe dataset to be: 1 Phase map - 1 intensity image.Along with the original InShaPe dataset, Yan, et al. also publicly released six optical simulation scripts [3] (also under CC BY 4.0 License) that generated the subsets of the six beam shapes. We used the Zernike coefficients given in the original InShaPe dataset of Yan, et al. to re-run the simulations and reproduced the aberrated SLM phase maps and distorted beam shape images in original size 1281×1281. We crop reproduced phase maps and beam shape images around the aperture to reduce matrices size to 427×427 to accelerate trainings. Therefore, each paired sample of the processed InShaPe dataset is an aberrated SLM phase map serving as the ground truth (supervision label) and a distorted beam shape intensity image serving as the input for phase retrieval. The same data processing is conducted on the six subsets of the six beam shapes, respectively, to obtain the same six training sets and test sets as original dataset.<br>References:[1] Shengyuan Yan, Richard Off, Anil Bora Yayak, Katrin Wudy, Anoush Aghajani-Talesh, Markus Birg, Jonas Grünewald, Mike Holenderski, and Nirvana Meratnia (2025). Large-scale multi-beamshape phase retrieval dataset based on Zernike coefficients for PBF-LB/M systems. Optica Publishing Group. Dataset. https://doi.org/10.6084/m9.figshare.27650703.v2[2] Shengyuan Yan, Richard Off, Anil Bora Yayak, Katrin Wudy, Anoush Aghajani-Talesh, Markus Birg, Jonas Grünewald, Mike Holenderski, and Nirvana Meratnia (2025). Deep learning based phase retrieval with complex beam shapes for beam shape correction. Optics Express <b>33</b>, 10806-10834. https://doi.org/10.1364/OE.547138[3] Shengyuan Yan, Richard Off, Anil Bora Yayak, Katrin Wudy, Anoush Aghajani-Talesh, Markus Birg, Jonas Grünewald, Mike Holenderski, and Nirvana Meratnia (2025). Codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems. Optica Publishing Group. Software. https://doi.org/10.6084/m9.figshare.27650946.v2<br>This simulated dataset containing six beam shapes is used for training and testing on the complex forward path application, PBF-LB/M beam shaping system, in our paper "Efficient Gerchberg-Saxton algorithm deep unrolling for phase retrieval with complex forward path". In this dataset, there are 6 sub-datasets stored in 6 zip folders named by "dense+[beamshape name]+30k_pre.zip". For each beam shape, the trainging set and the test set has 10000 and 3000 pairs of aberrated PBF-LB/M beam shape samples, respectively. Each paired sample contains one intensity profile of the aberrated beam shape in an imaging plane 5 times Rayleigh length before the back focal plane and one corresponding aberrated SLM phase mask that generated the aberrated beam shape. The aberrations in the paired samples are modelled with linear combination of Zernike polynomials amplified by randomly sampled Zernike coefficients.In order for the convenience of distributing the dataset, this repository does not contain the simulated paired samples. Instead, we give the original Zernike coefficients stored in Numpy array .npy files and the corresponding simulation script so that the users can run the simulation to reproduce the entire dataset.

版权声明：本数据集"面向FourierGSNet的预处理InShaPe数据集"改编自原InShaPe数据集[1]，原数据集由论文《基于深度学习的复杂光束形状相位检索与光束形状校正》[2]发布，遵循CC BY 4.0知识共享署名许可协议。
与原InShaPe数据集的修改说明：本数据集与原InShaPe数据集均为配对数据集。原InShaPe数据集的样本配对形式为：12个泽尼克系数（Zernike coefficients）——3张强度图像；而本数据集的样本配对形式已从原InShaPe数据集的对应样本修改为：1张相位图——1张强度图像。
除原InShaPe数据集外，Yan等人还公开发布了6个光学仿真脚本[3]（同样遵循CC BY 4.0许可协议），用于生成6种光束形状的子集。我们使用Yan等人原InShaPe数据集中提供的泽尼克系数重新运行仿真，复现了尺寸为1281×1281的畸变空间光调制器（Spatial Light Modulator, SLM）相位图与畸变光束形状图像。我们将复得的相位图与光束形状图像在孔径区域进行裁剪，将矩阵尺寸缩减至427×427，以加速训练流程。因此，本预处理InShaPe数据集的每一组配对样本均包含：一张作为真值（监督标签）的畸变SLM相位图，以及一张作为相位检索输入的畸变光束形状强度图像。我们对6种光束形状的所有子集分别执行了相同的数据处理流程，从而得到与原数据集一致的6个训练集与测试集。

参考文献：
[1] Shengyuan Yan, Richard Off, Anil Bora Yayak, Katrin Wudy, Anoush Aghajani-Talesh, Markus Birg, Jonas Grünewald, Mike Holenderski, and Nirvana Meratnia (2025). Large-scale multi-beamshape phase retrieval dataset based on Zernike coefficients for PBF-LB/M systems. Optica Publishing Group. Dataset. https://doi.org/10.6084/m9.figshare.27650703.v2
[2] Shengyuan Yan, Richard Off, Anil Bora Yayak, Katrin Wudy, Anoush Aghajani-Talesh, Markus Birg, Jonas Grünewald, Mike Holenderski, and Nirvana Meratnia (2025). Deep learning based phase retrieval with complex beam shapes for beam shape correction. Optics Express 33, 10806-10834. https://doi.org/10.1364/OE.547138
[3] Shengyuan Yan, Richard Off, Anil Bora Yayak, Katrin Wudy, Anoush Aghajani-Talesh, Markus Birg, Jonas Grünewald, Mike Holenderski, and Nirvana Meratnia (2025). Codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems. Optica Publishing Group. Software. https://doi.org/10.6084/m9.figshare.27650946.v2

本包含6种光束形状的仿真数据集被用于我们的论文《面向复杂前向路径的格施伯格-桑克森算法深度展开式相位检索》中针对复杂前向路径应用场景——PBF-LB/M光束整形系统的训练与测试。本数据集包含6个子数据集，分别存储于6个名为"dense+[光束形状名称]+30k_pre.zip"的压缩文件夹中。针对每种光束形状，其训练集与测试集分别包含10000组与3000组畸变PBF-LB/M光束形状样本。每一组配对样本均包含：一张位于后焦面前方5倍瑞利长度的成像平面内的畸变光束强度分布，以及一张生成该畸变光束的对应畸变SLM相位掩模。配对样本中的畸变通过泽尼克多项式的线性组合建模，并通过随机采样的泽尼克系数进行放大。
为方便数据集分发，本仓库未包含仿真得到的配对样本，仅提供存储于Numpy数组格式.npy文件中的原始泽尼克系数与对应的仿真脚本，用户可通过运行仿真脚本复现完整数据集。