Machine Learning-Based Diffractive Imaging with Subwavelength Resolution

We report detection and characterization of wavelength-scale objects with subwavelength resolution by combining diffractive imaging and machine learning. The technique clarifies the information channels in the diffraction imaging and provides insight into machine learning processes.

[1]  William P. Wardley,et al.  Interscale mixing microscopy: far-field imaging beyond the diffraction limit , 2016 .

[2]  Sandeep Inampudi,et al.  Interscale mixing microscopy: numerically stable imaging of wavelength- scale objects with sub-wavelength resolution and far field measurements. , 2015, Optics express.

[3]  H. Fripp On the Limits of the Optical Capacity of the Microscope , 1876 .

[4]  Andrew G. Glen,et al.  APPL , 2001 .

[5]  Z. Jacob,et al.  Optical Hyperlens: Far-field imaging beyond the diffraction limit. , 2006, Optics express.

[6]  Alessandro Salandrino,et al.  Far-field subdiffraction optical microscopy using metamaterial crystals: Theory and simulations , 2006 .

[7]  S. Thongrattanasiri,et al.  Analytical technique for subwavelength far field imaging , 2010 .

[8]  Michael J Rust,et al.  Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) , 2006, Nature Methods.

[9]  J. Elf,et al.  Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes , 2016, Science.

[10]  M. Gustafsson Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy , 2000, Journal of microscopy.

[11]  Yonina C Eldar,et al.  Super-resolution and reconstruction of sparse sub-wavelength images. , 2009, Optics express.

[12]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[13]  Evgenii Narimanov,et al.  Resolution limit of label-free far-field microscopy , 2019, Advanced Photonics.

[14]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[15]  B. Minasny The Elements of Statistical Learning, Second Edition, Trevor Hastie, Robert Tishirani, Jerome Friedman. (2009), Springer Series in Statistics, ISBN 0172-7397, 745 pp , 2009 .

[16]  Lord Rayleigh On the Theory of Optical Images, with Special Reference to the Microscope , 1903 .

[17]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[18]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[19]  Mark R. Dennis,et al.  A super-oscillatory lens optical microscope for subwavelength imaging. , 2012, Nature materials.

[20]  D. A. Dunnett Classical Electrodynamics , 2020, Nature.

[21]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[22]  Michael D. Mason,et al.  Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. , 2006, Biophysical journal.

[23]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[24]  Zhaowei Liu,et al.  Far-Field Optical Hyperlens Magnifying Sub-Diffraction-Limited Objects , 2007, Science.

[25]  Anne Sentenac,et al.  Beyond the Rayleigh criterion: grating assisted far-field optical diffraction tomography. , 2006, Physical review letters.

[26]  Xiaocong Yuan,et al.  Deep-subwavelength features of photonic skyrmions in a confined electromagnetic field with orbital angular momentum , 2018, Nature Physics.

[27]  Lester Curtis The Limits of the Optical Capacity of the Microscope , 1877, The American journal of dental science.

[28]  Zhaowei Liu,et al.  Superlenses to overcome the diffraction limit. , 2008, Nature materials.

[29]  S. Hell,et al.  Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. , 1994, Optics letters.

[30]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  E. Abbe Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung , 1873 .

[32]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.