Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Masking and Demasking Agents

Masking and Demasking Agents

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 the metal...

Deconvolution

Deconvolution

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...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

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...

Upsampling

Upsampling

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...

You might also read

Related Articles

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

Sort by

Same author

Locally tuned deformation fields combination for 2D cine-MRI-based driving of 3D motion models.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2021

Same author

Characterization of the Sweet Cherry Isolate of Plum Pox Potyvirus.

Plant disease·2019

Same author

Combined influence of CT random noise and HU-RSP calibration curve nonlinearities on proton range systematic errors.

Physics in medicine and biology·2017

Same author

First record of tomato yellow leaf curl Sardinia virus (TYLCSV) on pepper in Italy.

Communications in agricultural and applied biological sciences·2009

Same author

Three-dimensional computed tomography cephalometric craniofacial analysis: experimental validation in vitro.

International journal of oral and maxillofacial surgery·2007

Same author

Integrated management of TYLCV/TYLCSV on greenhouse hydroponic tomatoes in Southern Italy.

Communications in agricultural and applied biological sciences·2007

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

Postprocessing of images by filtering the unmasked coding noise.

S Comes¹, B Macq, M Mattavelli

¹Belgacom Co., Brussels, Belgium.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 13, 2008

Summary

This study introduces adaptive filtering to enhance image quality by reducing coding noise. The method uses a human visual system model, incorporating masking, to improve visual perception in processed images.

More Related Videos

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

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

Published on: July 11, 2025

Related Experiment Videos

Last Updated: Jul 7, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

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

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

Published on: July 11, 2025

Area of Science:

Image Processing
Computer Vision
Human Visual Perception

Background:

Digital images are often degraded by coding noise, impacting visual quality.
Existing methods may not fully account for human visual perception in noise reduction.

Purpose of the Study:

To develop a methodology for restoring visual quality in still images affected by coding noise.
To leverage a human visual system model, specifically the masking phenomenon, for adaptive filtering.

Main Methods:

Proposed an image transformation to generate visual stimuli aligned with a perceptual model.
Developed locally adaptive filtering based on a local measure of perceptual stimulus masking.
Investigated processing schemes for Discrete Cosine Transform (DCT) and subband coded images.
Explored DCT coding noise characteristics and blind neural estimation for noise assessment.

Main Results:

Experimental results demonstrate significant improvements in the visual quality of processed images.
The proposed perceptual model and filtering approach effectively reduce coding noise.
Validation of the adaptive filtering strategy based on human visual masking.

Conclusions:

The developed methodology effectively restores visual quality in noisy images.
The human visual system model, including masking, is crucial for perceptually relevant noise reduction.
The adaptive postprocessing filtering offers a promising solution for enhancing digital image quality.