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

Downsampling

Downsampling

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...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

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...

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...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

Active Filters

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:

You might also read

Related Articles

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

Sort by

Same author

A nationwide survey on the curriculum and educational resources related to the Clinical Skills Test of the Korean Medical Licensing Examination: a cross-sectional descriptive study.

Journal of educational evaluation for health professions·2025

Same author

Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm.

PeerJ. Computer science·2025

Same author

A data augmentation model integrating supervised and unsupervised learning for recommendation.

Scientific reports·2025

Same author

Evaluation of a basic-clinical integrated pharmacology case-based learning program from a student perspective using the CIPP model.

Korean journal of medical education·2024

Same author

Identifying alternately poisoning attacks in federated learning online using trajectory anomaly detection method.

Scientific reports·2024

Same author

PSAR-SR: Patches separation and artifacts removal for improving super-resolution networks.

Neural networks : the official journal of the International Neural Network Society·2024

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

A rank-ordered marginal filter for deinterlacing.

Gwanggil Jeon¹, Marco Anisetti, Seok Hoon Kang

¹Department of Embedded Systems Engineering, Incheon National University, Yeonsu-gu, Incheon, Korea. gjeon@incheon.ac.kr

Sensors (Basel, Switzerland)

|March 6, 2013

Summary

This summary is machine-generated.

This study introduces an improved deinterlacing filter that enhances edge preservation. The new method effectively interpolates missing lines, reducing visual artifacts for clearer images.

More Related Videos

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

Related Experiment Videos

Last Updated: May 13, 2026

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

Area of Science:

Digital Image Processing
Computer Vision

Background:

Deinterlacing is crucial for displaying interlaced video content on progressive displays.
Conventional edge-based methods often struggle with preserving image details and introducing artifacts.

Purpose of the Study:

To propose a novel interpolation filter for deinterlacing that improves edge preservation.
To reduce the introduction of visual artifacts during the deinterlacing process.

Main Methods:

The proposed filter employs a three-step approach: pre-processing, fuzzy metric-based weight assignment, and a rank-ordered marginal filter.
Enhancement of edge-preserving capabilities compared to traditional line averaging techniques.

Main Results:

The developed algorithm successfully interpolates missing video lines without generating noticeable artifacts.
Simulation results demonstrate superior performance in reducing visual artifacts compared to existing deinterlacing methods.

Conclusions:

The proposed deinterlacing filter offers enhanced edge preservation and artifact reduction.
This method provides a significant improvement for deinterlacing applications, leading to higher quality image reproduction.