A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution

TL;DR

This paper introduces FGIN, a single-image hyperspectral super-resolution model that effectively fuses spectral and spatial information without requiring precise image alignment, achieving competitive results.

Contribution

The paper presents a novel fusion-guided Inception network that integrates spectral-spatial fusion, multiscale feature extraction, and an optimized upsampling module for hyperspectral image super-resolution.

Findings

Demonstrates competitive performance on public datasets.

Effectively fuses spectral and spatial data without alignment.

Utilizes multiscale feature extraction for improved detail.

Abstract

The fusion of low-spatial-resolution hyperspectral images (HSIs) with high-spatial-resolution conventional images (e.g., panchromatic or RGB) has played a significant role in recent advancements in HSI super-resolution. However, this fusion process relies on the availability of precise alignment between image pairs, which is often challenging in real-world scenarios. To mitigate this limitation, we propose a single-image super-resolution model called the Fusion-Guided Inception Network (FGIN). Specifically, we first employ a spectral-spatial fusion module to effectively integrate spectral and spatial information at an early stage. Next, an Inception-like hierarchical feature extraction strategy is used to capture multiscale spatial dependencies, followed by a dedicated multi-scale fusion block. To further enhance reconstruction quality, we incorporate an optimized upsampling module that combines bilinear interpolation with depthwise separable convolutions. Experimental evaluations on two publicly available hyperspectral datasets demonstrate the competitive performance of our method.