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Fusion-based hyperspectral image (HSI) super-resolution has becomeincreasingly prevalent for its capability to integrate high-frequency spatialinformation from the paired high-resolution (HR) RGB reference image. However,most of the existing methods either heavily rely on the accurate alignmentbetween low-resolution (LR) HSIs and RGB images, or can only deal withsimulated unaligned RGB images generated by rigid geometric transformations,which weakens their effectiveness for real scenes. In this paper, we explorethe fusion-based HSI super-resolution with real RGB reference images that haveboth rigid and non-rigid misalignments. To properly address the limitations ofexisting methods for unaligned reference images, we propose an HSI fusionnetwork with heterogenous feature extractions, multi-stage feature alignments,and attentive feature fusion. Specifically, our network first transforms theinput HSI and RGB images into two sets of multi-scale features with an HSIencoder and an RGB encoder, respectively. The features of RGB reference imagesare then processed by a multi-stage alignment module to explicitly align thefeatures of RGB reference with the LR HSI. Finally, the aligned features of RGBreference are further adjusted by an adaptive attention module to focus more ondiscriminative regions before sending them to the fusion decoder to generatethe reconstructed HR HSI. Additionally, we collect a real-world HSI fusiondataset, consisting of paired HSI and unaligned RGB reference, to support theevaluation of the proposed model for real scenes. Extensive experiments areconducted on both simulated and our real-world datasets, and it shows that ourmethod obtains a clear improvement over existing single-image and fusion-basedsuper-resolution methods on quantitative assessment as well as visualcomparison.

Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance