Jove
Visualize
Contact Us
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 Concept Videos

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

180
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...
180
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
Reducing Line Loss01:18

Reducing Line Loss

168
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...
168
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.8K
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...
4.8K
Downsampling01:20

Downsampling

177
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
177

You might also read

Related Articles

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

Sort by
Same author

Association of body mass index and serum markers with immune-related adverse events in lung cancer patients receiving immune checkpoint inhibitors.

Journal of thoracic disease·2026
Same author

Preoperative MRI features for predicting response to postoperative adjuvant anti-PD-1 therapy in hepatocellular carcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2026
Same author

Promoting Electroreduction of Nitrate to Ammonia in Neutral Media via the Synergistic Effect of Atomically Dispersed Fe, Cu, and Pd Sites.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Downchirp Echo From a Stationary Water Surface Wave Field Accompanying Whirligig Beetle (Gyrinidae).

Integrative and comparative biology·2026
Same author

Chelating Anion-Mediated Solvation Structures for Rechargeable Magnesium Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Weizmannia coagulans BC99 improved intestinal motility and chronic constipation through regulating gut microbiota: a randomized, double-blind, placebo-controlled trial.

European journal of nutrition·2026

Related Experiment Video

Updated: Jul 15, 2025

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

568

A Survey of Deep Learning-Based Low-Light Image Enhancement.

Zhen Tian1,2, Peixin Qu1,2, Jielin Li1,2

  • 1School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This paper reviews deep learning methods for low-light image enhancement, addressing issues like poor brightness and noise. It covers network structures, datasets, and evaluation metrics for improving image quality.

Keywords:
deep learningimage degradationimage enhancementlow-light Images

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

Related Experiment Videos

Last Updated: Jul 15, 2025

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

568
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Low-light images suffer from reduced brightness, contrast, color distortion, and noise.
  • Effective low-light image enhancement is crucial for subsequent image processing tasks.

Purpose of the Study:

  • To provide a comprehensive review of deep learning-based low-light image enhancement.
  • To systematically introduce methods, datasets, and evaluation metrics in this field.

Main Methods:

  • Reviewing various deep learning network structures for low-light enhancement.
  • Describing low-light image quality evaluation methods.
  • Organizing and analyzing low-light image datasets.

Main Results:

  • Comparison and analysis of the advantages and disadvantages of existing deep learning methods.
  • Identification of key aspects including network architecture, training data, and evaluation metrics.

Conclusions:

  • Deep learning offers significant advancements in low-light image enhancement.
  • Future research directions are outlined for further development in the field.