Open AccessUltimate resolution limits of speckle-based compressive imaging
Author(s) -
Benjamin Lochocki,
Ksenia Abrashitova,
Johannes F. de Boer,
Lyubov V. Amitonova
Publication year - 2021
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.413831
Subject(s) - optics , speckle pattern , compressed sensing , image resolution , ghost imaging , nyquist frequency , iterative reconstruction , point spread function , pixel , image quality , resolution (logic) , microscopy , speckle noise , oversampling , scattering , digital micromirror device , physics , computer science , image (mathematics) , computer vision , artificial intelligence , optoelectronics , filter (signal processing) , cmos
Compressive imaging using sparsity constraints is a very promising field of microscopy that provides a dramatic enhancement of the spatial resolution beyond the Abbe diffraction limit. Moreover, it simultaneously overcomes the Nyquist limit by reconstructing an N-pixel image from less than N single-point measurements. Here we present fundamental resolution limits of noiseless compressive imaging via sparsity constraints, speckle illumination and single-pixel detection. We addressed the experimental setup that uses randomly generated speckle patterns (in a scattering media or a multimode fiber). The optimal number of measurements, the ultimate spatial resolution limit and the surprisingly important role of discretization are demonstrated by the theoretical analysis and numerical simulations. We show that, in contrast to conventional microscopy, oversampling may decrease the resolution and reconstruction quality of compressive imaging.