用单图提升高光谱图像分辨率，无需配准对齐。
A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution
- 早期融合光谱与空间信息，构建多尺度特征提取结构。
- 在两个公开数据集上达到优于现有方法的重建效果。
- 适合无精确配准图像对的高光谱图像增强任务。
将低空间分辨率高光谱图像（HSI）与高空间分辨率全色或RGB图像融合，是近年来提升高光谱图像超分辨率的重要手段。然而，该融合过程依赖于图像对间精确的配准，这在实际场景中往往难以实现。为缓解此问题，我们提出一种名为融合引导的残差膨胀网络（FGIN）的单图超分辨率模型。首先，采用光谱-空间融合模块在早期阶段有效整合光谱与空间信息；随后，通过类Inception的分层特征提取策略捕捉多尺度空间依赖，并引入专用多尺度融合模块。为进一步提升重建质量，设计了一种优化的上采样模块，结合双线性插值与深度可分离卷积。在两个公开高光谱数据集上的实验验证了该方法的优越性能。
原文摘要 · Abstract (English)
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.
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