Jove
Visualize
Contact Us

Related Concept Videos

Deconvolution01:20

Deconvolution

722
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...
722

You might also read

Related Articles

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

Sort by
Same author

Syntax-Guided Content-Adaptive Transform for Image Compression.

Sensors (Basel, Switzerland)ยท2024
See all related articles
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: Apr 15, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

Hybrid Sparse Transformer and Wavelet Fusion-Based Deep Unfolding Network for Hyperspectral Snapshot Compressive

Yangke Ying1, Jin Wang2, Yunhui Shi1

  • 1Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary

This study introduces a novel deep unfolding network for hyperspectral image reconstruction, enhancing feature representation and fusion. The method significantly improves reconstruction performance using sparse Transformers and wavelet fusion.

Keywords:
compressive sensingdeep unfolding networkhyperspectral image reconstructionsnapshot compressive imaging

More Related Videos

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
08:49

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

1.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376

Related Experiment Videos

Last Updated: Apr 15, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
08:49

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

1.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376

Area of Science:

  • Computer Vision
  • Signal Processing
  • Machine Learning

Background:

  • Deep unfolding networks show promise for hyperspectral snapshot compressive imaging.
  • Transformer models are increasingly used but often underutilize self-attention.
  • Existing methods lack effective intra-stage and inter-stage feature fusion.

Purpose of the Study:

  • To develop an advanced deep unfolding network for hyperspectral image reconstruction.
  • To enhance feature representation and fusion mechanisms in hyperspectral imaging algorithms.
  • To overcome limitations of current Transformer-based approaches in hyperspectral reconstruction.

Main Methods:

  • Hybridization of sparse Transformer and wavelet fusion within a deep unfolding network.
  • Development of spatial and spectral sparse Transformers for targeted attention.
  • Integration of wavelet-based methods for intra-stage and inter-stage feature fusion.

Main Results:

  • The proposed spatial and spectral sparse Transformers effectively capture HSI data attention.
  • Wavelet-based fusion significantly enhances intra-stage and inter-stage feature integration.
  • Experimental results demonstrate superior hyperspectral image reconstruction performance.

Conclusions:

  • The novel hybrid network advances hyperspectral image reconstruction capabilities.
  • Sparse Transformers and wavelet fusion are key components for improved performance.
  • This approach offers a superior solution for hyperspectral snapshot compressive imaging.