Calibration-free quantitative phase imaging using data-driven aberration modeling.

We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity and stability of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells and microbeads, benchmarking against the conventional method using background subtractions.

[1]  YongKeun Park,et al.  Time-multiplexed structured illumination using a DMD for optical diffraction tomography. , 2017, Optics letters.

[2]  Gunho Choi,et al.  Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. , 2018, Optics express.

[3]  H. Chung,et al.  Three-dimensional label-free observation of individual bacteria upon antibiotic treatment using optical diffraction tomography , 2019, bioRxiv.

[4]  R. Horstmeyer,et al.  Wide-field, high-resolution Fourier ptychographic microscopy , 2013, Nature Photonics.

[5]  Frank Dubois,et al.  Partial spatial coherence effects in digital holographic microscopy with a laser source. , 2004, Applied optics.

[6]  Kyoohyun Kim,et al.  Optical diffraction tomography techniques for the study of cell pathophysiology , 2016, 1603.00592.

[7]  Van Lam,et al.  Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. , 2017, Optics express.

[8]  E. Hayakawa,et al.  Real-time cholesterol sorting in Plasmodium falciparum-erythrocytes as revealed by 3D label-free imaging , 2020, Scientific Reports.

[9]  E. Wolf.  Three-dimensional structure determination of semi-transparent objects from holographic data , 1969 .

[10]  Shuai Li,et al.  Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery , 2018, ArXiv.

[11]  YongKeun Park,et al.  Real-time quantitative phase imaging with a spatial phase-shifting algorithm. , 2011, Optics letters.

[12]  C. Werner,et al.  Satellite radar interferometry: Two-dimensional phase unwrapping , 1988 .

[13]  C. Depeursinge,et al.  Quantitative phase imaging in biomedicine , 2012, 2012 Conference on Lasers and Electro-Optics (CLEO).

[14]  Yibo Zhang,et al.  Phase recovery and holographic image reconstruction using deep learning in neural networks , 2017, Light: Science & Applications.

[15]  Zahid Yaqoob,et al.  Speckle-field digital holographic microscopy , 2009, BiOS.

[16]  Yingjie Yu,et al.  Study on aberration suppressing methods in digital micro-holography , 2009 .

[17]  Yongkeun Park,et al.  Mitotic Chromosomes in Live Cells Characterized Using High-Speed and Label-Free Optical Diffraction Tomography , 2019, Cells.

[18]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[19]  M. Takeda,et al.  Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry , 1982 .

[20]  Rama Krishna Sai Subrahmanyam Gorthi,et al.  PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping , 2019, IEEE Signal Processing Letters.

[21]  B. Kemper,et al.  Digital holographic microscopy for live cell applications and technical inspection. , 2008, Applied optics.

[22]  Xiangnan Wang,et al.  Fast phase retrieval in off-axis digital holographic microscopy through deep learning. , 2018, Optics express.

[23]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[24]  Pietro Ferraro,et al.  Compensation of the inherent wave front curvature in digital holographic coherent microscopy for quantitative phase-contrast imaging. , 2003, Applied optics.

[25]  Huchuan Lu,et al.  Learning Dual Convolutional Neural Networks for Low-Level Vision , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Jianlin Zhao,et al.  One-step robust deep learning phase unwrapping. , 2019, Optics express.

[27]  H. Pham,et al.  Diffraction phase microscopy with white light. , 2012, Optics letters.

[28]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[30]  YongKeun Park,et al.  Optical imaging techniques for the study of malaria. , 2012, Trends in biotechnology.

[31]  Jonghee Yoon,et al.  Holographic deep learning for rapid optical screening of anthrax spores , 2017, Science Advances.

[32]  YongKeun Park,et al.  Active illumination using a digital micromirror device for quantitative phase imaging. , 2015, Optics letters.

[33]  Aydogan Ozcan,et al.  Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram , 2018, Light: Science & Applications.

[34]  Hyun-seok Min,et al.  Quantitative Phase Imaging and Artificial Intelligence: A Review , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[35]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[36]  Chang-Young Jang,et al.  PRMT6-mediated H3R2me2a guides Aurora B to chromosome arms for proper chromosome segregation , 2020, Nature Communications.

[37]  R. Gerchberg.  A practical algorithm for the determination of phase from image and diffraction plane pictures , 1972 .

[38]  George Barbastathis,et al.  Low Photon Count Phase Retrieval Using Deep Learning. , 2018, Physical review letters.

[39]  YongKeun Park,et al.  Effects of spatiotemporal coherence on interferometric microscopy. , 2017, Optics express.

[40]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[41]  Young-Jin Kim,et al.  Speckle reduction in quantitative phase imaging by generating spatially incoherent laser field at electroactive optical diffusers. , 2017, Optics express.

[42]  M. Mildner,et al.  Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.

[43]  YongKeun Park,et al.  Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration. , 2017, Optics express.

[44]  Jin Won Kim,et al.  Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs. , 2020, ACS nano.

[45]  Arkadiusz Kuś,et al.  3D-printed biological cell phantom for testing 3D quantitative phase imaging systems , 2019, Scientific Reports.

[46]  Kyoohyun Kim,et al.  White-light quantitative phase imaging unit. , 2016, Optics express.

[47]  H. Hobara,et al.  Effect of step frequency on leg stiffness during running in unilateral transfemoral amputees , 2020, Scientific Reports.