Jove
Visualize
Contact Us

Related Concept Videos

Retrieval01:12

Retrieval

444
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
444
ER Retrieval Pathway01:45

ER Retrieval Pathway

4.8K
In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
4.8K
Phase Diagrams02:39

Phase Diagrams

50.1K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
50.1K
Phase Transitions02:31

Phase Transitions

23.1K
Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
23.1K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Inductance: Single-Phase And Three-Phase Line01:28

Inductance: Single-Phase And Three-Phase Line

626
Understanding the inductance of transmission lines is crucial for efficient design and operation in electrical power systems. This discussion delves into the inductance characteristics of single-phase two-wire and three-phase three-wire transmission lines with equal phase spacing.
Single-Phase Two-Wire Line:
A single-phase line consists of two solid cylindrical conductors, denoted as x and y. Each conductor carries phasor currents ix and iy, respectively. Given that the sum of these currents is...
626

You might also read

Related Articles

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

Sort by
Same author

Interpretable Deep Learning for Single-Molecule Nanopore Fingerprinting Using Physics-Guided Preprocessing.

ACS sensors·2026
Same author

AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma.

Investigative ophthalmology & visual science·2026
Same author

Static DFOS: pushing towards the limits of chirped-pulse phase-sensitive OTDR.

Optics express·2025
Same author

Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion.

Nature communications·2025
Same author

Sensitivity fields and parameter estimation from dielectric objects.

Journal of the Optical Society of America. A, Optics, image science, and vision·2025
Same author

Biomechanics-Function in Glaucoma: Improved Visual Field Predictions from IOP-Induced Neural Strains.

American journal of ophthalmology·2024
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 31, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K

Low Photon Count Phase Retrieval Using Deep Learning.

Alexandre Goy1, Kwabena Arthur1, Shuai Li1

  • 1Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Physical Review Letters
|January 5, 2019
PubMed
Summary
This summary is machine-generated.

Deep neural networks enhance imaging systems by recovering objects under low light conditions. This method outperforms traditional algorithms, even with minimal photons per pixel.

More Related Videos

Deep-Tissue Three-Photon Fluorescence Microscopy in Intact Mouse and Zebrafish Brain
08:26

Deep-Tissue Three-Photon Fluorescence Microscopy in Intact Mouse and Zebrafish Brain

Published on: January 13, 2022

5.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

Related Experiment Videos

Last Updated: Jan 31, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
Deep-Tissue Three-Photon Fluorescence Microscopy in Intact Mouse and Zebrafish Brain
08:26

Deep-Tissue Three-Photon Fluorescence Microscopy in Intact Mouse and Zebrafish Brain

Published on: January 13, 2022

5.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

Area of Science:

  • Optics and Photonics
  • Computational Imaging
  • Machine Learning Applications

Background:

  • Shot noise significantly degrades imaging system performance at low light intensities.
  • Classical algorithms like Gerchberg-Saxton struggle with extremely low signal-to-noise ratios.

Purpose of the Study:

  • To demonstrate deep neural networks (DNNs) for object recovery in low-light imaging.
  • To compare DNN performance against the Gerchberg-Saxton algorithm under equivalent signal-to-noise ratios.
  • To explore the impact of training data and initial estimates on DNN performance for phase retrieval.

Main Methods:

  • Experimental demonstration of DNNs for phase retrieval with weak light illumination.
  • Utilizing prior information from training image sets within the DNN.
  • Comparing DNNs with the Gerchberg-Saxton algorithm at signal-to-noise ratios near one.
  • Investigating the effect of training with an object's initial estimate versus raw intensity measurements.

Main Results:

  • DNNs show superior performance compared to the Gerchberg-Saxton algorithm for low-light object recovery.
  • Successful feature recovery was achieved with as few as one photon per detector pixel on average.
  • DNNs effectively leverage training data to detect features at signal-to-noise ratios close to one.
  • Training DNNs with an initial object estimate significantly improves phase reconstruction.

Conclusions:

  • Deep neural networks offer a powerful solution for overcoming shot noise limitations in low-light imaging.
  • DNNs can reconstruct object features from extremely low photon counts, enabling imaging in previously challenging conditions.
  • The inclusion of prior information and initial estimates during DNN training enhances phase retrieval accuracy.