Jove
Visualize
Contact Us

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Light Acquisition02:16

Light Acquisition

8.5K
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.
8.5K
Deconvolution01:20

Deconvolution

189
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
189
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

6.1K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
6.1K
Reducing Line Loss01:18

Reducing Line Loss

173
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
173
Focusing of Light in the Eye01:16

Focusing of Light in the Eye

2.9K
Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Fecal microbiota transplantation to reduce immune activation in ART-treated people with HIV with low CD4/CD8 ratio: protocol for the single-blind, randomized, placebo-controlled Gutsy study (CIHR/CTN PT038).

Trials·2025
Same author

Extracellular acyl-CoA-binding protein as an independent biomarker of COVID-19 disease severity.

Frontiers in immunology·2025
Same author

Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.

Molecular genetics & genomic medicine·2020
Same author

The Effects of P75NTR on Learning Memory Mediated by Hippocampal Apoptosis and Synaptic Plasticity.

Current pharmaceutical design·2020
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: Jul 20, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

802

Learnability Enhancement for Low-Light Raw Image Denoising: A Data Perspective.

Hansen Feng, Lizhi Wang, Yuzhi Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 3, 2023
    PubMed
    Summary

    This study enhances low-light raw image denoising by reforming data to overcome learnability limitations. The strategy improves image quality and model performance by addressing noise and data issues.

    More Related Videos

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.7K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    571

    Related Experiment Videos

    Last Updated: Jul 20, 2025

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    802
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.7K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    571

    Area of Science:

    • Computational photography
    • Image processing
    • Machine learning

    Background:

    • Learning-based methods are mainstream for low-light raw image denoising.
    • Current methods face learnability bottlenecks due to limited data, complex noise, and poor data quality.

    Purpose of the Study:

    • To introduce a learnability enhancement strategy for low-light raw image denoising.
    • To address the limitations of paired real data mapping.

    Main Methods:

    • Reforming paired real data using noise modeling.
    • Integrating shot noise augmentation (SNA) to increase data volume.
    • Implementing dark shading correction (DSC) to reduce noise complexity.
    • Developing an improved image acquisition protocol to enhance data quality.

    Main Results:

    • Shot noise augmentation (SNA) promotes data mapping precision.
    • Dark shading correction (DSC) enhances data mapping accuracy.
    • The developed image acquisition protocol improves data mapping reliability.
    • Experiments demonstrate the strategy's superiority on public and new datasets.

    Conclusions:

    • The proposed learnability enhancement strategy significantly improves low-light raw image denoising.
    • The integrated methods (SNA, DSC, new protocol) effectively overcome existing bottlenecks.
    • The new dataset facilitates further research in low-light image denoising.