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: May 14, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

End-to-End Image Demosaicking via Region-Level Non-Local Modeling and Residual Aggregation.

Lingyun Wei1, Han Liu1

  • 1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Cervical microbial dysbiosis in single HPV16-positive HSIL.

Scientific reports·2026
Same author

Garland Rolling Circle Amplification Mediated Self-Priming Extension Strategy for Sensitive and Label-Free <i>Pseudomonas aeruginosa</i> Analysis in Perioperative Period.

Journal of microbiology and biotechnology·2026
Same author

Dynamic weight learning for RGB image demosaicking with a Bayer color filter array.

Applied optics·2026
Same author

Multidimensional regulation of estrogen signaling in pelvic floor connective tissue homeostasis and remodeling.

Frontiers in immunology·2026
Same author

Effectiveness of Different Educational Approaches in Improving Comprehensive Clinical Competence Among Medical Students: A Systematic Review and Meta-Analysis.

Evaluation & the health professions·2026
Same author

Biological Aging Exhibits an Inverted J-Shaped Relationship with Stress Urinary Incontinence in Women.

International journal of women's health·2025
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

This study introduces RNRA-Net, a novel deep learning approach for image demosaicking. It effectively reconstructs full-color images by preserving spatial phase relationships and enhancing details.

Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Image demosaicking reconstructs full-color images from sparse sensor data.
  • Existing deep learning methods struggle with CFA sampling structures and cross-position dependencies.

Purpose of the Study:

  • To propose an end-to-end image demosaicking network, RNRA-Net, that addresses limitations of current methods.
  • To preserve spatial phase relationships and improve reconstruction of high-frequency details.

Main Methods:

  • RNRA-Net decomposes mosaic images into a three-channel representation at original resolution, avoiding disruptive half-resolution packing.
  • A region-level non-local module captures feature correlations within bounded regions for contextual information.
  • A residual aggregation module refines early compensation features for edge and texture recovery.
Keywords:
color filter arrayimage demosaickingimage reconstructionnon-local modelingresidual aggregation

Related Experiment Videos

Last Updated: May 14, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Main Results:

  • RNRA-Net effectively preserves CFA spatial phase relationships.
  • The network demonstrates superior reconstruction of edges, textures, and high-frequency details.
  • Experiments on benchmark and high-resolution datasets validate the method's effectiveness.

Conclusions:

  • RNRA-Net offers an improved approach to image demosaicking by addressing sampling-structure constraints.
  • The proposed region-level non-local modeling and residual aggregation enhance image reconstruction quality.