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

Blind Procedures02:07

Blind Procedures

12.3K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
12.3K
Reducing Line Loss01:18

Reducing Line Loss

215
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...
215
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.7K
Introduction to Learning01:18

Introduction to Learning

591
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
591
Classification of Signals01:30

Classification of Signals

993
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
993
Observational Learning01:12

Observational Learning

360
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
360

You might also read

Related Articles

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

Sort by
Same author

Predicting DNA damage response using synthetic cell painting profiles and experimental analysis.

iScience·2026
Same author

A deep learning framework for finger motion recognition using forearm ultrasound imaging.

Scientific reports·2025
Same author

Correction: Precise phenotyping method using image data for carcass marbling score in Hanwoo cattle.

PloS one·2025
Same author

Precise phenotyping method using image data for carcass marbling score in Hanwoo cattle.

PloS one·2025
Same author

Features of bacterial and fungal communities in the rhizosphere of <i>Gastrodia elata</i> cultivated in greenhouse for early harvest.

Frontiers in microbiology·2024
Same author

Increased CD16a (FcγRIIIA) Expression in The Tumor Microenvironment of Atypical Neurofibromatous Neoplasms of Uncertain Biologic Potential May Be Associated with Progression from Neurofibromas to Atypical Neurofibromas.

Journal of personalized medicine·2023

Related Experiment Video

Updated: Oct 5, 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

672

Blind and Compact Denoising Network Based on Noise Order Learning.

Keunsoo Ko, Yeong Jun Koh, Chang-Su Kim

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 27, 2022
    PubMed
    Summary

    A new lightweight blind image denoiser, the blind compact denoising network (BCDNet), offers superior performance with minimal complexity. This efficient network effectively removes noise using adaptive filtering guided by noise severity.

    Related Experiment Videos

    Last Updated: Oct 5, 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

    672

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Blind image denoising is crucial for image quality enhancement.
    • Existing methods often struggle with a balance between performance and computational complexity.
    • Developing lightweight yet effective denoising models remains a significant challenge.

    Purpose of the Study:

    • To introduce a novel lightweight blind image denoiser, the blind compact denoising network (BCDNet).
    • To achieve a superior trade-off between denoising performance and network complexity.
    • To enable efficient and effective noise removal in digital images.

    Main Methods:

    • The BCDNet comprises a compact denoising network (CDNet) and a guidance network (GNet).
    • GNet extracts noise severity features from noisy images.
    • CDNet adaptively filters images based on guidance features for noise reduction.

    Main Results:

    • BCDNet demonstrates state-of-the-art or competitive denoising performance across various datasets.
    • The proposed network achieves this with significantly fewer parameters (only 330K).
    • CDNet effectively reduces network complexity without compromising denoising efficacy.

    Conclusions:

    • BCDNet offers an efficient and effective solution for blind image denoising.
    • The proposed architecture achieves excellent performance with remarkable parameter efficiency.
    • This work advances lightweight deep learning models for image restoration tasks.