CNN-based algorithm for improving the quality of Single-Shot Holographic Tomography - dataset

The data used to train the models mentioned in "CNN-based algorithm for improving the quality of Single-Shot Holographic Tomography". It contains real and simulated data, which had their Fourier spectra masked and exhibit the associated artifacts. This also contains their GT counterparts with the full spectrum, and thus without artifacts. This dataset contains Experimental data: volumes with experimental 3D RI reconstructions: microspheres, HaCaT, lymphocyte cells and many others [3, 4]. Technical data: technical objects (cell phantoms) [1, 2] and their augmentations numerical synthesis of the cell phantoms and its variations numerically generated objects - spheres, cubes, merged shapes The dataset is balanced between experimental and synthetic data. [1] M. Ziemczonok, A. Kuś, P. Wasylczyk, and M. Kujawińska, “3d-printed biological cell phantom for testing 3d quantitative phase imaging systems,” Sci. Reports 9, 1–9 (2019).[2] M. Ziemczonok, A. Kuś, and M. Kujawińska, “Optical diffraction tomography meets metrology — measurement accuracy on cellular and subcellular level,” Measurement 195, 111106 (2022).[3] M. Baczewska, W. Krauze, A. Kuś, P. Stępień, K. Tokarska, K. Zukowski, E. Malinowska, Z. Brzózka, and M. Kujawińska, “On-chip holographic tomography for quantifying refractive index changes of cells’ dynamics,” in Quantitative Phase Imaging VIII, vol. 11970 Y. Liu, G. Popescu, and Y. Park, eds., International Society for Optics and Photonics (SPIE, 2022), p. 1197008.[4] P. Stępień, M. Ziemczonok, M. Kujawińska, M. Baczewska, L. Valenti, A. Cherubini, E. Casirati, and W. Krauze, “Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging,” Biomed. Opt. Express 13, 5709–5720 (2022).