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Edge-Distilled and Local-Global Feature Selection Network for Hyperspectral Image Super-Resolution.

Xinzhao Li1, Mengzhe Fan1, Xiaoqing Zheng1

  • 1National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China.

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|February 13, 2026
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Summary
This summary is machine-generated.

A new Edge-Distilled and Local-Global Feature Selection network (EDLGFS) improves hyperspectral image super-resolution. It effectively extracts edge details and integrates local-global features for enhanced reconstruction quality.

Keywords:
deep learningedge-distilledhyperspectral imagelocal–global feature selectionsuper-resolution

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Remote Sensing

Background:

  • Convolutional neural networks show progress in hyperspectral image super-resolution.
  • Existing methods struggle with extracting edge details and capturing both local and global features.

Purpose of the Study:

  • To propose an Edge-Distilled and Local-Global Feature Selection network (EDLGFS) for hyperspectral image super-resolution.
  • To enhance super-resolution reconstruction quality by leveraging edge details and local-global features.

Main Methods:

  • An edge-guided super-resolution network using knowledge distillation to transfer edge information.
  • A Local-Global Feature Selection mechanism (LGFS) integrating multi-size convolutions and self-attention.
  • A dynamic loss mechanism to balance loss term contributions.

Main Results:

  • The proposed EDLGFS network demonstrates superior super-resolution reconstruction quality.
  • Experiments were conducted on three public datasets, validating the method's effectiveness.

Conclusions:

  • The EDLGFS network effectively addresses limitations in hyperspectral image super-resolution.
  • The integration of edge distillation and local-global feature selection significantly improves reconstruction quality.