A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution
This addresses a practical limitation in hyperspectral imaging for remote sensing or similar applications, but it appears incremental as it builds on existing fusion and network techniques.
The paper tackles the problem of hyperspectral image super-resolution without requiring aligned image pairs by proposing a single-image model called FGIN, which achieves competitive performance on two public datasets.
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.