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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

8.0K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
8.0K
Upsampling01:22

Upsampling

324
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...
324
Deconvolution01:20

Deconvolution

263
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...
263

You might also read

Related Articles

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

Sort by
Same author

Cardiovascular Complications in Hematopoietic Stem Cell Transplanted Patients.

Journal of personalized medicine·2022
Same author

Effect of Alkali and Sulfate on the Hydration Characteristic of Cement-Based Materials Containing Coal Gasification Slag.

Materials (Basel, Switzerland)·2022
Same author

Effects of Simultaneous Application of Double Chelating Agents to Pb-Contaminated Soil on the Phytoremediation Efficiency of <i>Indocalamus decorus</i> Q. H. Dai and the Soil Environment.

Toxics·2022
Same author

[Protective effect of active vitamin D on liver fibrosis induced by sodium arsenite in SD rats].

Wei sheng yan jiu = Journal of hygiene research·2022
Same author

Coronatine promotes maize water uptake by directly binding to the aquaporin ZmPIP2;5 and enhancing its activity.

Journal of integrative plant biology·2022
Same author

Hyperphosphorylation of EGFR/ERK signaling facilitates long-term arsenite-induced hepatocytes epithelial-mesenchymal transition and liver fibrosis in sprague-dawley rats.

Ecotoxicology and environmental safety·2022

Related Experiment Video

Updated: Sep 19, 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

651

Boosting lightweight single image super-resolution via global prior feature.

Rui He1, Zhenyang Zhu2, Xiaoyang Mao2

  • 1School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi, Address, kofu, Yamanashi, 400-8510, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|June 5, 2025
PubMed
Summary

This study introduces a new lightweight network for single image super-resolution (SISR) using vision transformers (ViT). The proposed global feature prior self-attention network enhances texture detail and structural accuracy, outperforming existing methods.

Keywords:
Global featureLightweight networkPrior informationSuper-resolution

More Related Videos

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

8.6K
Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion
13:11

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Published on: February 10, 2023

1.6K

Related Experiment Videos

Last Updated: Sep 19, 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

651
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

8.6K
Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion
13:11

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Published on: February 10, 2023

1.6K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Lightweight vision transformer (ViT)-based single image super-resolution (SISR) methods are gaining traction.
  • Aggressive parameter reduction in existing lightweight networks often compromises performance, leading to artifacts and texture blurring.
  • Transformers excel at global feature extraction but struggle with local details and high-frequency information.

Purpose of the Study:

  • To propose a novel global feature prior self-attention network (GFPSAN) to improve the performance of lightweight SISR networks.
  • To address the limitations of conventional window-based self-attention by focusing on texture-related pixels.
  • To enhance the extraction of critical information, structural details, local features, and high-frequency information.

Main Methods:

  • Leveraging prior knowledge to identify and apply self-attention specifically to texture-related pixels within windows.
  • Introducing an efficient global feature extraction method to capture essential information and structural details.
  • Integrating a local complementary module with shift window attention to compensate for transformers' weaknesses in local and high-frequency feature extraction.

Main Results:

  • The proposed GFPSAN method significantly outperforms existing state-of-the-art lightweight SISR approaches.
  • The method effectively mitigates artifacts and texture blurring common in other lightweight networks.
  • Experimental results validate the enhanced ability to capture both global and local features, including high-frequency details.

Conclusions:

  • The novel GFPSAN architecture offers a superior solution for lightweight single image super-resolution.
  • Prior knowledge-guided self-attention and complementary local feature extraction are effective strategies for improving SISR performance.
  • The proposed method represents a significant advancement in efficient and high-performance image super-resolution techniques.