Circumventing the optical diffraction limit with customized speckles

Bender, Nicholas; Sun, Mengyuan; Yılmaz, Hasan; Bewersdorf, Joerg; Cao, Hui

doi:10.1101/2020.07.31.230821

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…The key advantage of our concept is that it renders possible any form of widefield microscopy at the tip of a hair thin strand of MMF. This includes localisation-based super-resolution imaging [22,23], along with other emerging forms of parallelised super-resolution microscopy [71], structured illumination microscopy [72] and singleobjective light sheet microscopy [73]. Single-shot widefield imaging at any distance beyond the distal end of a short length of MMF also becomes possible.…”

Section: Discussionmentioning

confidence: 99%

How to Build the “Optical Inverse” of a Multimode Fibre

et al. 2022

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When light propagates through multimode optical fibres (MMFs), the spatial information it carries is scrambled. Wavefront shaping reverses this scrambling, typically one spatial mode at a time—enabling deployment of MMFs as ultrathin microendoscopes. Here, we go beyond sequential wavefront shaping by showing how to simultaneously unscramble all spatial modes emerging from an MMF in parallel. We introduce a passive multiple-scattering element—crafted through the process of inverse design—that is complementary to an MMF and undoes its optical effects. This “optical inverter” makes possible single-shot widefield imaging and super-resolution imaging through MMFs. Our design consists of a cascade of diffractive elements, and can be understood from the perspective of both multi-plane light conversion, and as a physically inspired diffractive neural network. This physical architecture outperforms state-of-the-art electronic neural networks tasked with unscrambling light, as it preserves the phase and coherence information of optical signals flowing through it. We show, in numerical simulations, how to efficiently sort and tune the relative phase of up to ~400 step-index fibre modes, reforming incoherent images of scenes at arbitrary distances from the fibre facet. Our optical inverter can dynamically adapt to see through experimentally realistic flexible fibres—made possible by moulding optical memory effects into its design. The scheme is based on current fabrication technology so could be realised in the near future. Beyond imaging, these concepts open up a range of new avenues for optical multiplexing, communications, and computation in the realms of classical and quantum photonics.

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Section: Discussionmentioning

confidence: 99%

How to Build the “Optical Inverse” of a Multimode Fibre

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The key advantage of our concept is that it renders possible any form of wide-field microscopy at the tip of a hair thin strand of MMF. This includes localisation-based superresolution imaging [18,19], along with other emerging forms of parallelised super-resolution microscopy [62], and structured illumination microscopy [63]. Single-shot wide-field imaging at any distance beyond the distal end of a short length of MMF also becomes possible.…”

Section: Discussionmentioning

confidence: 99%

How to build the optical inverse of a multimode fibre

Būtaitė¹,

Kupianskyi²,

Čižmár³

et al. 2022

Preprint

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When light propagates through a multimode optical fibre (MMF), the spatial information it carries is scrambled. Wavefront shaping can undo this scrambling, typically one spatial mode at a time -enabling deployment of MMFs as ultra-thin micro-endoscopes. In this work we go beyond serial wavefront shaping by showing how to simultaneously unscramble all spatial modes emerging from an MMF in parallel. We introduce a passive multiple-scattering element -crafted through the process of inverse design -that is complementary to an MMF and undoes its optical effects. This 'optical inverter' makes possible both single-shot wide-field imaging and super-resolution imaging through MMFs. Our design consists of a cascade of diffractive elements, and can be understood from the perspective of both multi-plane light conversion, and as a physically inspired deep diffractive neural network. This physical architecture can outperform state-of-the-art electronic neural networks tasked with unscrambling light, as it preserves the phase and coherence information of the optical signals flowing through it. Here we demonstrate our MMF inversion concept through numerical simulations, and efficiently sort and unscramble up to ∼ 400 step-index fibre modes, reforming incoherent images of scenes at arbitrary distances from the distal fibre facet. We also describe how our optical inverter can dynamically adapt to see through flexible fibres with a range of experimentally realistic TMs -made possible by moulding optical memory effects into the structure of our design. Although complex, our inversion scheme is based on current fabrication technology so could be realised in the near future. Beyond imaging through scattering media, these concepts open up a range of new avenues for optical multiplexing, communications and computation in the realms of classical and quantum photonics.

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“…These special characteristics give speckle patterns with unpredicted potential in many areas [1]. It has been widely used in measurement, bioimaging, and metrology [2][3][4]. Owing to the strong wavelength dependent characteristic of the speckle patterns, it is also employed as the carrier of light wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Recovering Wavelengths from highly compressed speckle patterns by Deep learning

Wang

Tao

Wang

et al. 2022

Preprint

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Recovering the wavelengths from disordered speckle patterns has become an exciting prospect as a wavelength measurement method due to its high resolution and simple design. In previous studies, panel cameras were often utilized as the speckle images receiver. However, high cost (especially at near infrared range), large size and low speed limit its application in optical communications, metrology and optical sensing. In this work, the speckle patterns were highly compressed into four intensities by using a quadrant detector (QD). And wavelengths still can be recovered through only four pixels instead of millions of pixels of a camera. A new CNN based demodulation algorithm, shallow residual network (SRN), was proposed to recognize the wavelengths from the highly compressed speckle images. Finally, a wavelength precision of 4 fm (~ 0.5 MHz) with an updating speed of ~ 1 kHz was achieved in the demonstrations. In addition, the SRN shows a broad measurement range and good noise robustness. Compared with camera based system, the QD detection scheme associated with the CNN algorithm provides a compact, high speed, and low cost method to examine the speckle patterns, which opens new routes in many other fields.

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Circumventing the optical diffraction limit with customized speckles

Cited by 4 publications

References 40 publications

How to Build the “Optical Inverse” of a Multimode Fibre

How to Build the “Optical Inverse” of a Multimode Fibre

How to build the optical inverse of a multimode fibre

Recovering Wavelengths from highly compressed speckle patterns by Deep learning

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