HYDRA: HYbrid knowledge Distillation and spectral Reconstruction Algorithm for high channel hyperspectral camera applications

• URL: http://arxiv.org/abs/2510.16664v1
• Date: Sat, 18 Oct 2025 23:29:30 GMT
• Title: HYDRA: HYbrid knowledge Distillation and spectral Reconstruction Algorithm for high channel hyperspectral camera applications
• Authors: Christopher Thirgood, Oscar Mendez, Erin Ling, Jon Storey, Simon Hadfield,
• Abstract summary: This paper introduces a novel approach to spectral reconstruction via our HYbrid knowledge Distillation and spectral Reconstruction Architecture (HYDRA)<n>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, we achieve high-quality spectral reconstruction.<n>This addresses key limitations of prior SR models, providing SOTA performance across all metrics, including an 18% boost in accuracy, and faster inference times than current SOTA models at various channel depths.
• Score: 11.883404434697809
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral images (HSI) promise to support a range of new applications in computer vision. Recent research has explored the feasibility of generalizable 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 generalizable 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, and faster inference times than current SOTA models at various channel depths.

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