Hyperspectral images (HSI) promise to support a range of new applications in computer vision.

Recent research has explored the feasibility of Spectral Reconstruction (SR), the problem of recovering a HSI from a natural three-channel color image in unseen scenarios.

However, previous Multi-Scale Attention (MSA) works have only demonstrated sufficient results for very sparse spectra, while modern HSI sensors contain hundreds of channels.

This paper introduces a novel approach to spectral reconstruction via our HYbrid knowledge Distillation and spectral Reconstruction Architecture (HYDRA).

Using a Teacher model that encapsulates latent hyperspectral image data and a Student model that learns mappings from natural images to the Teacher's encoded domain, alongside a novel training method, we achieve high-quality spectral reconstruction.

This addresses key limitations of prior SR models, providing SOTA performance across all metrics, including an 18\% boost in accuracy, with a favorable accuracy-efficiency trade-off.