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

## TL;DR

This paper introduces a new network for improving the resolution of hyperspectral images by better capturing edge details and combining local and global features.

## Contribution

The paper proposes a novel network combining edge distillation and a local–global feature selection mechanism for hyperspectral image super-resolution.

## Key findings

- The proposed EDLGFS network outperforms existing methods in reconstructing hyperspectral images.
- The edge-guided knowledge distillation improves the extraction of edge details in super-resolution.
- The LGFS mechanism effectively captures both local and global features for better image reconstruction.

## Abstract

In recent years, the methods based on convolutional neural networks have achieved significant progress in hyperspectral image super-resolution. However, existing methods still face two key challenges: (1) they fail to fully extract edge detail information from hyperspectral images; (2) they struggle to simultaneously capture local and global features. To address these issues, we propose an Edge-Distilled and Local–Global Feature Selection network (EDLGFS) for hyperspectral image super-resolution. This network aims to effectively leverage edge details and local–global features, thereby enhancing super-resolution reconstruction quality. Firstly, we design an edge-guided super-resolution network based on knowledge distillation. This network transfers edge knowledge to improve the reconstruction. Secondly, we propose a Local–Global Feature Selection mechanism (LGFS), which integrates convolutions of different sizes with the self-attention mechanism. This design models spatial correlations across features with different receptive fields, achieving efficient feature selection to more effectively capture local and global features. Finally, we propose a dynamic loss mechanism to more effectively balance the contribution of each loss term. Extensive experimental results on three public datasets demonstrate that the proposed EDLGFS achieves superior super-resolution reconstruction quality.