Far-field intensity signature of sub-wavelength microscopic objects

Abstract

Information about microscopic objects with features smaller than the diffraction limit is almost entirely lost in a far-field diffraction image but could be partly recovered with data completition techniques. Any such approach critically depends on the level of noise. This new path to superresolution has been recently investigated with the use of compressed sensing and machine learning. We demonstrate a two-stage technique based on deconvolution and genetic optimization which enables the recovery of objects with features of 1/10 of the wavelength. We indicate that l1-norm based optimization in the Fourier domain unrelated to sparsity is more robust to noise than its l2-based counterpart. We also introduce an extremely fast general-purpose restricted domain calculation method for Fourier transform based iterative algorithms operating on sparse data.

References in corpus (10)

• The optical microscopy with virtual image breaks a record: 50-nm resolution imaging is demonstrated
• Deep Learning Microscopy
• Sparsity-based single-shot sub-wavelength coherent diffractive imaging
• Structured illumination microscopy using a photonic chip
• Structured illumination microscopy with unknown patterns and a statistical prior
• Near-Field Nanoprobing Using Si Tip-Au Nanoparticle Photoinduced Force Microscopy with 120:1 Signal-to-Noise Ratio, Sub-6-nm Resolution
• Reconstruction of purely absorbing, absorbing and phase-shifting, and strong phase-shifting objects from their single-shot in-line holograms
• Coherent microscopy at resolution beyond diffraction limit using post-experimental data extrapolation
• What is superresolution microscopy?
• Interscale Mixing Microscopy: numerically stable imaging of wavelength- scale objects with sub- wavelength resolution and far field measurements