Diffract and Conquer: Hyperspectral Imaging from Any RGB Camera via Optical Encoding and Learning

Abstract

Hyperspectral imaging (HSI) provides detailed spectral information but is often impractical due to high cost, size, and hardware complexity. While learning-based methods attempt to recover hyperspectral images from RGB sensors, they are constrained by limited spectral measurements and noise. We propose a unified optical–computational approach that converts any standard RGB camera into a snapshot hyperspectral imaging system. Our method introduces a diffractive optical adapter that replaces the conventional lens with a diffractive lens array optimized for spectral encoding. To reconstruct hyperspectral images from the resulting measurements, we design a neural network specialized for diffractively encoded RGB data, capable of compensating for optical distortions and recovering high-quality spectra. The proposed system achieves up to 10 dB improvement in PSNR over RGB-based hyperspectral reconstruction and enhances the performance of existing state-of-theart models by up to 6 dB when used with the proposed adapter. Our results demonstrate that diffractive optical encoding combined with learned reconstruction enables practical and scalable hyperspectral imaging using commodity RGB cameras.