Wave front shaping, the ability to manipulate light fields both spatially and temporally, in complex media is an emerging field with many applications. This review summarizes how insights from mesoscopic scattering theory have direct relevance for optical wave control experiments and vice versa. The results are expected to have an impact on a number of fields ranging from biomedical imaging to nanophotonics, quantum information, and communication technology.

Light fields in complex media: Mesoscopic scattering meets wave control

Light scattering was thought to be the fundamental limitation for the depth at which optical imaging methods can retain their resolution and sensitivity. However, it was shown that light can be focused inside even the most strongly scattering objects by spatially shaping the wavefront of the incident light. This review summarizes recently developed feedback-based approaches for focusing light inside and through scattering objects

/pdf/feedback-based-wavefront-shaping-3llj93xp3l.pdf

Feedback-based wavefront shaping.

Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent light propagating within them to produce seemingly random patterns. Thus, for applications such as imaging and image projection through an MMF, careful measurements of the relationship between the inputs and outputs of the fiber are required. We show, as a proof of concept, that a deep neural network can learn the input-output relationship in a 0.75 m long MMF. Specifically, we demonstrate that a deep convolutional neural network (CNN) can learn the nonlinear relationships between the amplitude of the speckle pattern (phase information lost) obtained at the output of the fiber and the phase or the amplitude at the input of the fiber. Effectively, the network performs a nonlinear inversion task. We obtained image fidelities (correlations) as high as ~98% for reconstruction and ~94% for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix. We further show that the network can be trained for transfer learning, i.e., it can transmit images through the MMF, which belongs to another class not used for training/testing. A convolutional neural network (CNN) can successfully learn the nonlinear transmission characteristics of a multimode fibre thus allowing accurate image transmission and reconstruction. Propagation along a multimode fibre usually scrambles an input image, resulting in a seemingly random speckle pattern at the output. Babak Rahmani and coworkers from the Ecole Polytechnique Federale de Lausanne in Switzerland have now shown that a deep neural network(either a 22-layer CNN based on VGG-net technology or a 20-layer CNN based on Res-net technology) can learn the input-output relationship in a 0.75 m long piece of multimode fibre) and thus undo this scrambling. Experiments showed that both neural networks could perform highly accurate image reconstruction with an image fidelity as high as ~98% for image reconstruction and ~94% for image projection in the best case.

/pdf/multimode-optical-fiber-transmission-with-a-deep-learning-4qx1t0g7zn.pdf

Multimode optical fiber transmission with a deep learning network.

Progress in neuroscience constantly relies on the development of new techniques to investigate the complex dynamics of neuronal networks. An ongoing challenge is to achieve minimally-invasive and high-resolution observations of neuronal activity in vivo inside deep brain areas. A perspective strategy is to utilise holographic control of light propagation in complex media, which allows converting a hair-thin multimode optical fibre into an ultra-narrow imaging tool. Compared to current endoscopes based on GRIN lenses or fibre bundles, this concept offers a footprint reduction exceeding an order of magnitude, together with a significant enhancement in resolution. We designed a compact and high-speed system for fluorescent imaging at the tip of a fibre, achieving micron-scale resolution across a 50 um field of view, and yielding 7-kilopixel images at a rate of 3.5 frames/s. Furthermore, we demonstrate in vivo observations of cell bodies and processes of inhibitory neurons within deep layers of the visual cortex and hippocampus of anesthetised mice. This study forms the basis for several perspective techniques of modern microscopy to be delivered deep inside the tissue of living animal models while causing minimal impact on its structural and functional properties.

/pdf/high-fidelity-multimode-fibre-based-endoscopy-for-deep-brain-406ixvcc03.pdf

High-fidelity multimode fibre-based endoscopy for deep-brain in vivo imaging

We observe a nonlinear spatial self-cleaning process for femtosecond pulses in graded-index (GRIN) multimode fiber (MMF). Pulses with ∼80 fs duration at 1030 nm are launched into GRIN MMF with 62.5 μm core. The near-field beam profile at the output end of the fiber evolves from a speckled pattern to a centered, bell-shaped transverse structure with increasing pulse energy. The experimental observations agree well with numerical simulations, which show that the Kerr nonlinearity underlies the process. This self-cleaning process may find applications in ultrafast pulse generation and beam-combining.

Kerr self-cleaning of femtosecond-pulsed beams in graded-index multimode fiber.

Multimode optical fibers are attractive for biomedical and sensing applications because they possess a small cross section and can bend over small radii of curvature. However, mode phase-velocity dispersion and random mode coupling change with bending, temperature, and other perturbations, producing scrambling interference among propagating modes; hence preventing its use for focusing or imaging. To tackle this problem we introduce a system capable of re-focusing light through a multimode fiber in 37ms, one order of magnitude faster than demonstrated in previous reports. As a result, the focus spot can be maintained during significant bending of the fiber, opening numerous opportunities for endoscopic imaging and energy delivery applications. We measure the transmission matrix of the fiber by projecting binary-amplitude computer generated holograms using a digital micro-mirror device controlled by a field programmable gate array. The system shows two orders of magnitude enhancements of the focus spot relative to the background.

Real-time resilient focusing through a bending multimode fiber

Controlling the propagation and interaction of light in complex media has sparked major interest in the last few years. Unfortunately, spatial light modulation devices suffer from limited speed that precludes real-time applications such as imaging in live tissue. To address this critical problem we introduce a phase-control technique to characterize complex media based on the use of fast 1D spatial light modulators and a 1D-to-2D transformation performed by the same medium being analyzed. We implement the concept using a micro-electro-mechanical grating light valve (GLV) with 1088 degrees of freedom modulated at 350 KHz, enabling unprecedented high-speed wavefront measurements. We continuously measure the transmission matrix, calculate the optimal wavefront and project a focus through various dynamic scattering samples in real-time, all within 2.4 ms per cycle. These results improve prior wavefront shaping modulation speed by more than an order of magnitude and open new opportunities for optical processing using 1D-to-2D transformations.

Wavefront shaping in complex media at 350 KHz with a 1D-to-2D transform

Controlling the propagation and interaction of light in complex media has sparked major interest in the past few years. Unfortunately, spatial light modulation devices suffer from limited speed, which precludes real-time applications such as imaging in live tissue. To address this critical problem, we introduce a phase-control technique to characterize complex media based on the use of fast one-dimensional (1D) spatial light modulators and a 1D-to-2D transformation performed by the same medium being analysed. We implement the concept using a microelectromechanical grating light valve with 1,088 degrees of freedom, modulated at 350 kHz, enabling unprecedented high-speed wavefront measurements. We continuously measure the transmission matrix, calculate the optimal wavefront and project a focus through various dynamic scattering samples in real time, all within 2.4 ms per cycle. These results improve the previously achieved wavefront shaping modulation speed by more than an order of magnitude and open new opportunities for optical processing using 1D-to-2D transformations. A phase-control technique based on the use of fast one-dimensional (1D) spatial light modulators and a 1D-to-2D transformation enables high-speed wavefront measurements and manipulation in complex media, facilitating real-time applications such as imaging in live tissue.

Wavefront shaping in complex media with a 350 kHz modulator via a 1D-to-2D transform

The optical imaging depth in biological materials is limited by the scattering of light in tissue. New methods which control light propagation through scattering media have been introduced with the potential to overcome the scattering of light in biological materials. These techniques shape the incident wavefront to pre-compensate for the scattering effects of light propagation in the material and beyond. However, living biological materials have speckle decorrelation times on the millisecond timescale. This fast rate of change makes liquid crystal spatial light modulation (LC-SLM) devices too slow for this task. To achieve the required wavefront control with high modulation speeds we present binary-amplitude off-axis computer-generated holography implemented on a digital micro-mirror device (DMD). Binary amplitude offaxis holography is a method for the generation of arbitrary wavefronts, and in particular uniform-amplitude phasemodulated images. As a result, we are able to simultaneously encode phase modulated wavefronts at the high frame rate of binary amplitude DMDs. This wavefront encoding technique allows for focusing through temporally dynamic turbid materials at a rate which approaches the decorrelation time of living biological tissue. We demonstrate this technique by high speed wavefront optimization for focusing through turbid media as well as through a dynamic, strongly scattering sample with short speckle decorrelation times. With this approach we attain an order of magnitude improvement in measurement speed over the previous fastest wavefront determination method and three orders of magnitude improvement over LC-SLM methods.

High-speed phase-control of wavefronts with binary amplitude DMD for light control through dynamic turbid media

Focusing inside scattering media is a challenging task with a variety of applications in biomedicine. State of the art methods mostly require invasive feedback inside or behind the sample, limiting practical use. We present a technique for dynamic control and focusing inside scattering media that combines two powerful methods: optical coherence tomography (OCT) and wave-front shaping (WFS). We use OCT as a non-invasive feedback for WFS optimization of a separate, penetrating laser. Energy absorbed in the sample, creates thermal expansions that are used for the feedback mechanism. By maximizing thermal deformations within a selected focal region, we demonstrate enhanced focusing of light through scattering media beyond the ballistic regime and within the penetration range of OCT.

Thermal expansion feedback for wave-front shaping

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Eyal Niv

Author Tools

Chat about Author