Hyperkan: An Interpretable Approximation Network for Hyperspectral Superresolution

In hyperspectral super-resolution (HSR), multivariate continuous functions can approximate the nonlinear spectrum mapping. This process is typically managed using multilayer perceptrons (MLPs), which operate as black boxes. However, these opaque operations make it difficult to interpret whether the super-resolution results align with the underlying physical mechanisms of imaging. Building on the Kolmogorov-Arnold representation theorem, which states that any complex nonlinear function can be constructed through the simple superposition of multiple linear functions, an interpretable super-resolution KAN network, termed HyperKAN, is proposed. This model leverages a univariate function, parametrized as a spline, to directly model the continuous spectral sequence without additional nonlinearities. HyperKAN is effectively constrained by the spectral function, allowing for intuitive visualization, supervision, and control over each node within the neural network. We conducted experiments on CAVE and Chikusei datasets to evaluate the effectiveness of the proposed model achieves state-of-the-art (SOTA) performance compared to competitive methods.