Lightweight super-resolution multimode fiber imaging with regularized linear regression.

Super-resolution multimode fiber imaging provides the means to image samples quickly with compact and flexible setups finding many applications from biology and medicine to material science and nanolithography. Typically, fiber-based imaging systems suffer from low spatial resolution and long measurement times. State-of-the-art computational approaches can achieve fast super-resolution imaging through a multimode fiber probe but currently rely on either per-sample optimised priors or large data sets with subsequent long training and image reconstruction times. This unfortunately hinders any real-time imaging applications. Here we present an ultimately fast non-iterative algorithm for compressive image reconstruction through a multimode fiber. The proposed approach helps to avoid many constraints by determining the prior of the target distribution from a simulated set and solving the under-determined inverse matrix problem with a mathematical closed-form solution. We have demonstrated theoretical and experimental evidence for enhanced image quality and sub-diffraction spatial resolution of the multimode fiber optical system.

Super-resolution multimode fiber imaging with an untrained neural network.

Multimode fiber endoscopes provide extreme miniaturization of imaging components for minimally invasive deep tissue imaging. Typically, such fiber systems suffer from low spatial resolution and long measurement time. Fast super-resolution imaging through a multimode fiber has been achieved by using computational optimization algorithms with hand-picked priors. However, machine learning reconstruction approaches offer the promise of better priors, but require large training datasets and therefore long and unpractical pre-calibration time. Here we report a method of multimode fiber imaging based on unsupervised learning with untrained neural networks. The proposed approach solves the ill-posed inverse problem by not relying on any pre-training process. We have demonstrated both theoretically and experimentally that untrained neural networks enhance the imaging quality and provide sub-diffraction spatial resolution of the multimode fiber imaging system.

Sub-diffraction computational imaging via a flexible multicore-multimode fiber.

An ultra-thin multimode fiber is an ideal platform for minimally invasive microscopy with the advantages of a high density of modes, high spatial resolution, and a compact size. In practical applications, the probe needs to be long and flexible, which unfortunately destroys the imaging capabilities of a multimode fiber. In this work, we propose and experimentally demonstrate sub-diffraction imaging through a flexible probe based on a unique multicore-multimode fiber. A multicore part consists of 120 Fermat's spiral distributed single-mode cores. Each of the cores offers stable light delivery to the multimode part, which provides optimal structured light illumination for sub-diffraction imaging. As a result, perturbation-resilient fast sub-diffraction fiber imaging by computational compressive sensing is demonstrated.

High-speed label-free multimode-fiber-based compressive imaging beyond the diffraction limit.

Glass fibers are miniature optical components that serve as ultra-narrow endoscopy probes. Ideally, one would want to perform imaging through a fiber at the highest achievable resolution and speed. State-of-the-art super-resolution techniques have shattered the diffraction limit, but more than twofold improvement requires fluorescent labeling and a long acquisition time. Moreover, it is challenging to implement super-resolution microscopy in a fiber format. Here we present fiber-based label-free video-rate imaging at more than 2-fold higher resolution than the diffraction limit. Our work paves the way to rapid, sub-wavelength endo-microscopy in unlabeled live specimens.

Ultimate resolution limits of speckle-based compressive imaging.

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.

Multimode Interference of Bloch Surface Electromagnetic Waves.

Integrated photonics aims at on-chip controlling light in the micro- and nanoscale ranges utilizing the waveguide circuits, which include such basic elements as splitters, multiplexers, and phase shifters. Several photonic platforms, including the well-developed silicon-on-insulator and surface-plasmon polaritons ones, operate well mostly in the IR region. However, operating in the visible region is challenging because of the drawbacks originating from absorption or sophisticated fabrication technology. Recently, a new promising all-dielectric platform based on Bloch surface electromagnetic waves (BSWs) in multilayer structures and functioning in the visible range has emerged finding a lot of applications primarily in sensing. Here, we show the effect of multimode interference (MMI) of BSWs and propose a method for implementing the advanced integrated photonic devices on the BSW platform. We determine the main parameters of MMI effect and demonstrate the operation of Mach-Zehnder interferometers with a predefined phase shift proving the principle of MMI BSW-based photonics in the visible spectrum. Our research will be useful for further developing a versatile toolbox of the BSW platform devices which can be essential in integrated photonics, lab-on-chip, and sensing applications.

Optical performance and radiation stability of polymer X-ray refractive nano-lenses.

Full-field X-ray imaging and microscopy with polymer compound refractive nano-lenses is demonstrated. Experiments were carried out at beamline ID13 at the European Synchrotron and yielded a resolution of 100 nm. The lenses were demonstrated to be functioning even after an absorbed dose of ∼107 Gy. This article also discusses issues related to lens aberrations, astigmatism and radiation stability, and thus ways of improving the lens further are considered. Polymer nano-lenses are versatile and are promissing for nano-focusing and compact X-ray microscopy.