Modulate and Reconstruct: Learning Hyperspectral Imaging from Misaligned Smartphone Views

Abstract

Hyperspectral reconstruction (HSR) from RGB images is a highly promising direction for accurate color reproduction and material color measurement. While most existing approaches rely on a single RGB image - thereby limiting reconstruction accuracy - the majority of modern smartphones are equipped with two or more cameras. In this work, we propose a novel multi-image-to-hyperspectral reconstruction (MI-HSR) framework that leverages a triple-camera smartphone system, where two lenses are equipped with carefully selected spectral filters. Our easy-to-implement configuration, based on theoretical and empirical analysis, allows to obtain more complete and diverse spectral data than traditional single-chamber setups. To support this new paradigm, we introduce Doomer, the first dataset for MI-HSR, comprising aligned images from three smartphone cameras and a hyperspectral reference camera across diverse scenes. We further introduce a lightweight alignment module for MI-HSR that effectively fuses multi-view inputs while mitigating parallax- and occlusion-induced artifacts. Proposed module demonstrate consistent quality improvements for modern HSR methods. In a nutshell, our setup allows 30% more accurate estimations of spectra compared to an ordinary RGB camera, while the proposed alignment module boosts the reconstruction quality of SotA methods by an additional 5%. Our findings suggest that spectral filtering of multiple views with commodity hardware unlocks more accurate and practical hyperspectral imaging.