Physics-embedded learning for interpretable, generalized, and rapid speckle reconstructive spectroscopy
Speckle reconstructive spectrometers (RSs) leverage the wavelength sensitivity of disordered media for spectral encoding, where the spectral information must be decoded via computational reconstruction algorithms. In recent years, machine learning has emerged as a powerful computational tool and has been applied to spectral reconstruction. However, existing learning-based methods predominantly rely on multi-layer artificial neural network architectures, which suffer from limited physical interpretability and insufficient generalization across diverse spectral types. Grounded in the insights of spectral-to-spatial mapping, especially its linear scattering nature, we introduce a physics-embedded model whose structure implicitly represents the inverse of this mapping. Compared with the standard architecture of convolutional neural networks and multilayer perceptrons, our learning-based method features a task-oriented design that incorporates domain knowledge of RSs, demonstrating superior generalization capability and a 30-fold improvement in inference speed. The proposed approach offers significant potential in applications such as real-time optical monitoring and hyperspectral imaging, among others.