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 Experiment Video

Updated: Jan 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

Image super-resolution reconstruction algorithm based on multi-scale recursive attention and feature fusion.

Haixia Liu1, Mingliang Wang2,3

  • 1Keyi College, Zhejiang Sci-Tech University, Shaoxing, China.

Plos One
|October 7, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.2K
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...
12.2K

You might also read

Related Articles

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

Sort by
Same author

Atomic-level protein-ligand recognition with PBCNet2.0 for probe discovery.

Nature chemical biology·2026
Same author

A dual-layer vector map encryption scheme using 4D hyperchaos and SM4.

Scientific reports·2026
Same author

Neural Wave Propagation for Surgical Video Action Recognition: A New Dataset and Baseline.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

The relationship between background parenchymal enhancement, amount of fibroglandular tissue and synchronous contralateral breast cancer in preoperative magnetic resonance imaging of newly diagnosed breast cancer patients.

BMC medical imaging·2026
Same author

Discovery of Novel Alkynylbenzene Scaffold-Based PTPN2-Selective Degrader PD-305 with Exceptional Potency and In Vivo Efficacy.

Journal of medicinal chemistry·2026
Same author

Strong 3D-printed aluminium reinforced with ductile-transformable eutectic nano-skeleton.

Nature communications·2026
Same journal

Thymidylate synthase inhibitory drugs induce p53-dependent pathways differently.

PloS one·2026
Same journal

Top-down and bottom-up attention for joint pattern classification and reconstruction.

PloS one·2026
Same journal

Short- and long-term scaling behavior of blood pressure and pulse arrival time during sleep in healthy controls and patients with obstructive sleep apnea.

PloS one·2026
Same journal

Double DQN-based secrecy energy efficiency and fairness performance in IRS-assisted NOMA systems with friendly jamming.

PloS one·2026
Same journal

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century.

PloS one·2026
Same journal

Paying in public: Peer effects, impression management, and willingness to pay on digital payment platforms.

PloS one·2026
See all related articles

This study introduces a novel algorithm combining multi-scale feature extraction and attention feature fusion for enhanced image super-resolution. The proposed method significantly improves reconstruction accuracy and visual quality in complex environments.

Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Image super-resolution reconstruction is vital but challenged by complex environmental interferences causing image distortion.
  • Existing methods struggle with accuracy and robustness in real-world scenarios.

Purpose of the Study:

  • To develop an advanced image super-resolution algorithm addressing distortion and improving reconstruction quality.
  • To enhance the accuracy and robustness of image reconstruction for computer vision applications.

Main Methods:

  • Innovatively combined multi-scale feature extraction (MSFE) and attention feature fusion (AFF).
  • Developed the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net).
  • Optimized network structure by increasing module depth and branch complexity.

Related Experiment Videos

Last Updated: Jan 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

Main Results:

  • MSRAFFB significantly improved overall algorithm performance and reconstruction quality over baseline methods.
  • MSRAFFB-Net effectively reduced reconstruction error and enhanced perceptual quality.
  • The algorithm demonstrated high accuracy across magnification factors and preserved original image information in real-world scenarios.

Conclusions:

  • The proposed algorithm enhances accuracy and robustness in image reconstruction.
  • This advancement has positive implications for computer vision in high-resolution image processing applications.