  Hyperspectral image (HSI) super-resolution without additional auxiliary image
remains a constant challenge due to its high-dimensional spectral patterns,
where learning an effective spatial and spectral representation is a
fundamental issue. Recently, Implicit Neural Representations (INRs) are making
strides as a novel and effective representation, especially in the
reconstruction task. Therefore, in this work, we propose a novel HSI
reconstruction model based on INR which represents HSI by a continuous function
mapping a spatial coordinate to its corresponding spectral radiance values. In
particular, as a specific implementation of INR, the parameters of parametric
model are predicted by a hypernetwork that operates on feature extraction using
convolution network. It makes the continuous functions map the spatial
coordinates to pixel values in a content-aware manner. Moreover, periodic
spatial encoding are deeply integrated with the reconstruction procedure, which
makes our model capable of recovering more high frequency details. To verify
the efficacy of our model, we conduct experiments on three HSI datasets (CAVE,
NUS, and NTIRE2018). Experimental results show that the proposed model can
achieve competitive reconstruction performance in comparison with the
state-of-the-art methods. In addition, we provide an ablation study on the
effect of individual components of our model. We hope this paper could server
as a potent reference for future research.
