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

Deconvolution01:20

Deconvolution

655
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...
655
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.7K
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...
8.7K
Upsampling01:22

Upsampling

676
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
676

You might also read

Related Articles

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

Sort by
Same author

Optimizing graft weight prediction in living donor liver transplantation: a machine learning approach integrating pathologic, radiological and biological factors.

HPB : the official journal of the International Hepato Pancreato Biliary Association·2026
Same author

Feasibility of an artificial intelligence based fractional flow reserve assessment for coronary artery disease.

Coronary artery disease·2026
Same author

Artificial Intelligence-Enabled Short-Term Ambulatory Monitoring ECG During Sinus Rhythm for Prediction of Hidden Atrial Fibrillation.

Journal of cardiovascular electrophysiology·2025
Same author

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

Cancer imaging : the official publication of the International Cancer Imaging Society·2024
Same author

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.

La Radiologia medica·2023
Same author

Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis.

European journal of clinical investigation·2023
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

515

Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.

Chin-Chang Yang, Shu-Mei Guo, Jason Sheng-Hong Tsai

    IEEE Transactions on Cybernetics
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an evolutionary fuzzy block-matching algorithm for camera raw image denoising. The new method enhances detail preservation and outperforms existing algorithms in noise removal.

    More Related Videos

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    3.4K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
    07:12

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

    Published on: January 6, 2026

    515
    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    3.4K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Variance stabilization transforms are common for camera raw image denoising.
    • Existing methods in stabilized domains can cause excessive blurring of image details.
    • Signal-dependent noise in camera sensors requires effective denoising strategies.

    Purpose of the Study:

    • To develop an advanced image denoising algorithm for camera raw images.
    • To improve detail preservation compared to existing denoising techniques.
    • To enhance the performance of image denoising using novel block-matching and fuzzy logic.

    Main Methods:

    • Proposed an evolutionary fuzzy block-matching algorithm for image denoising.
    • Utilized a type-2 fuzzy logic system (FLS) to identify similar image blocks.
    • Employed differential evolution to optimize the denoising process.
    • Averaged similar blocks with FLS-determined weightings for noise reduction.

    Main Results:

    • The proposed algorithm effectively removes noise from camera raw images.
    • Demonstrated superior performance in subjective and objective image quality measures.
    • Outperformed two state-of-the-art image denoising algorithms.

    Conclusions:

    • The evolutionary fuzzy block-matching algorithm offers improved image denoising.
    • The method effectively balances noise reduction and detail preservation.
    • This approach represents a significant advancement in camera raw image processing.