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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

79
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
79
Upsampling01:22

Upsampling

161
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...
161
Downsampling01:20

Downsampling

109
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...
109
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

145
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...
145
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

56
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
56
Aliasing01:18

Aliasing

100
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
100

You might also read

Related Articles

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

Sort by
Same author

Flow-Multi: A Flow-Matching Multi-Reward Framework for Text-to-Image Generation.

Sensors (Basel, Switzerland)·2026
Same author

Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Global Context Attention for Robust Visual Tracking.

Sensors (Basel, Switzerland)·2023
Same author

Single Camera-Based Dual-Channel Near-Infrared Fluorescence Imaging system.

Sensors (Basel, Switzerland)·2022
Same author

Development of Intraoperative Near-Infrared Fluorescence Imaging System Using a Dual-CMOS Single Camera.

Sensors (Basel, Switzerland)·2022
Same author

Test-Time Adaptation for Video Frame Interpolation via Meta-Learning.

IEEE transactions on pattern analysis and machine intelligence·2021
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

Related Experiment Video

Updated: May 10, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

FDI-VSR: Video Super-Resolution Through Frequency-Domain Integration and Dynamic Offset Estimation.

Donghun Lim1, Janghoon Choi1

  • 1Graduate School of Data Science, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces FDI-VSR, a novel framework for video super-resolution (VSR) that enhances video quality by integrating spatiotemporal dynamics and frequency-domain analysis. The method significantly improves visual fidelity and outperforms existing VSR techniques.

Keywords:
dynamic offset estimationfrequency-domain integrationspatiotemporal feature extractionvideo super-resolution

More Related Videos

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.6K
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.2K

Related Experiment Videos

Last Updated: May 10, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.6K
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.2K

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • High-resolution imaging sensors drive demand for advanced video quality enhancement.
  • Single-image super-resolution (SISR) methods applied to videos neglect temporal information, causing inconsistencies.
  • Existing video super-resolution (VSR) methods often struggle with temporal coherence and global context.

Purpose of the Study:

  • To develop a novel video super-resolution (VSR) framework, FDI-VSR, that integrates spatiotemporal dynamics and frequency-domain analysis.
  • To improve video quality by addressing limitations of traditional SISR methods when applied to video sequences.
  • To achieve superior VSR performance with reduced computational complexity.

Main Methods:

  • Proposed FDI-VSR framework integrating Spatiotemporal Feature Extraction Module (STFEM) and Frequency-Spatial Integration Module (FSIM).
  • STFEM utilizes dynamic offset estimation, spatial alignment, and multi-stage temporal aggregation with residual channel attention blocks (RCABs).
  • FSIM transforms deep features into the frequency domain for enhanced global context capture.

Main Results:

  • FDI-VSR surpasses conventional VSR methods and achieves competitive results against state-of-the-art approaches.
  • Demonstrated improvements of up to 0.82 dB in PSNR on the SPMCs benchmark.
  • Achieved notable reductions in visual artifacts with lower computational complexity and faster inference.

Conclusions:

  • FDI-VSR effectively enhances video quality by leveraging spatiotemporal information and frequency-domain analysis.
  • The proposed method offers a significant advancement in video super-resolution technology.
  • FDI-VSR provides a computationally efficient and high-performance solution for VSR applications.