Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

Differential Leveling

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Demonstration of <b>3</b>.5 × 10<sup>-13</sup> laser frequency stability at 1000 s using an iodine-filled hollow-core fiber photonic microcell.

Optics express·2026

Same author

Compressive spectral video by dynamic spatial-spectral-temporal windowed codification.

Optics express·2026

Same author

Boosting microparticle tracking with neuromorphic cameras by optical modulation.

Scientific reports·2025

Same author

Scalable Coding for High-Resolution, High-Compression Ratio Snapshot Compressive Video.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

Same author

Single-mask sphere-packing with implicit neural representation reconstruction for ultrahigh-speed imaging.

Optics express·2025

Same author

Snapshot video through dynamic scattering medium based on deep learning.

Optics express·2025

Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026

Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026

Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026

Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026

Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026

Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Bringing the Visible Universe into Focus with Robo-AO

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

Boosting the deep learning wavefront sensor for real-time applications [Invited].

Esteban Vera, Felipe Guzmán, Camilo Weinberger

|April 2, 2021

Summary

This summary is machine-generated.

Deep learning wavefront sensors (DLWFS) can now be trained faster and estimate Zernike coefficients more accurately using smaller image sizes. This optimization speeds up prediction time for real-time adaptive optics.

More Related Videos

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Related Experiment Videos

Last Updated: Nov 10, 2025

Bringing the Visible Universe into Focus with Robo-AO

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Area of Science:

Optical Engineering
Machine Learning

Background:

Deep learning wavefront sensors (DLWFS) directly estimate Zernike coefficients from intensity images.
Massive convolutional neural networks (CNNs) traditionally used in DLWFS require extensive training and estimation times.

Purpose of the Study:

To reduce the training and estimation time of DLWFS.
To explore the impact of smaller input image sizes and varying numbers of Zernike modes on performance.

Main Methods:

Developed a rapidly trainable CNN for DLWFS.
Investigated the use of reduced input image resolutions (8x8, 16x16, 32x32).
Evaluated the estimation of different numbers of Zernike modes.

Main Results:

The proposed CNN significantly reduces training time.
Smaller input image sizes (8x8, 16x16, 32x32) dramatically decrease training duration.
Estimation accuracy of Zernike coefficients is maintained or improved with smaller image sizes.
A 16x16 DLWFS accurately estimates the first 12 Zernike coefficients (excluding piston, tip, tilt) with accelerated prediction.

Conclusions:

Optimized DLWFS using smaller input sizes offers a viable solution for faster training and estimation.
This approach facilitates the development of low-cost, real-time adaptive optics systems.