Search research articles

Related Concept Videos

Deconvolution

Deconvolution

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...

You might also read

Related Articles

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

Sort by

Same author

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026

Same author

Deep Error-Aware Iterative Optimization Network for Broadband Mosaiced Hyperspectral Imaging.

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

Same author

Orientation-Guided Homography for Fine-Grained Cross-View Localization.

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

Same author

Spectral-Spatial Dynamic Scan Mamba for Multi-Source Remote Sensing Data Classification.

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

Same author

Real-CD: Change Detection Under Real-World Complex Interference via Dynamic Distribution Correction.

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

Same author

Spectral-Spatial-Temporal Kolmogorov-Arnold Network for Hyperspectral Change Detection.

IEEE transactions on neural networks and learning systems·2026

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 2, 2026

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

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

Published on: December 1, 2023

Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Image Fusion.

Renwei Dian, Shutao Li, Leyuan Fang

IEEE Transactions on Cybernetics

|December 4, 2019

Summary

This study introduces a new nonlocal sparse tensor factorization method for fusing hyperspectral and multispectral images. The approach enhances spatial resolution by leveraging self-similarities within hyperspectral data, outperforming existing methods in semiblind scenarios.

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

A Multimodal Wide-Field Fourier-Transform Raman Microscope

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Related Experiment Videos

Last Updated: Jan 2, 2026

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

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

Published on: December 1, 2023

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

A Multimodal Wide-Field Fourier-Transform Raman Microscope

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Area of Science:

Remote Sensing
Image Processing
Data Fusion

Background:

Hyperspectral image (HSI) spatial resolution enhancement commonly uses high-spatial-resolution multispectral images (HR-MSI).
Existing fusion methods based on matrix factorization may not fully exploit the 3D structure of HSI data.
Nonlocal self-similarities in HSI are a key characteristic for advanced fusion techniques.

Purpose of the Study:

To develop a novel semiblind fusion method for HSI and MSI.
To improve upon existing matrix factorization techniques by utilizing tensor decomposition.
To address the limitations of current methods in handling sensor point spread functions (PSFs).

Main Methods:

Proposed a nonlocal sparse tensor factorization approach (NLSTF_SMBF) for HSI and MSI fusion.
Decomposed HSI into full-band patches (FBPs) and factored them into dictionaries and a sparse core tensor.
Clustered similar FBPs to share dictionaries, exploiting nonlocal self-similarities and performing tensor sparse coding.

Main Results:

The NLSTF_SMBF method demonstrated superior performance compared to state-of-the-art fusion techniques.
The method effectively handles semiblind fusion scenarios, including spatially variant point spread functions (PSFs).
Experimental results validated the advantages of the proposed tensor factorization approach.

Conclusions:

NLSTF_SMBF offers a robust and effective solution for enhancing HSI spatial resolution.
The method's ability to be blind to the sensor's PSF is a significant advancement.
This approach provides a powerful tool for analyzing hyperspectral and multispectral data in complex imaging conditions.