Sensor-Adaptive Infrared Spectral Reconstruction with Plug-and-Play Diffusion Priors

Alireza Siyavashi, Jon Schlipf, Sebastian Reiter, Inga Fischer, Christian Wenger, Christian Herglotz

Hyperspectral sensing enables material identification; however, state-of-the-art spectrometers are costly and bulky, which limits their use in mobile applications. We address this by proposing sparse spectrum reconstruction from narrowband photocurrents using a pseudoinverse-guided diffusion model (ΠGDM). With ΠGDM we use a denoising diffusion probabilistic model (DDPM) to reconstruct the spectrum, which is trained on a large public spectral dataset to learn realistic spectral priors, eliminating the need for paired sensor measurements. At inference, ΠGDM alternates reverse-diffusion denoising steps with pseudoinverse projection to enforce consistency with measured photocurrents via the calibrated responsivity matrices of sensors. Consequently, our method is sensor-adaptive: when detector arrays change, we simply substitute the responsivity matrix in the pseudoinverse projection without retraining of the diffusion model. The resulting computational spectrometer achieves 1.502% average estimation error, outperforming Tikhonov, Gaussian, compressive-sensing, and multilayer perceptron (MLP) baselines, while providing calibrated uncertainty estimates via Monte Carlo sampling from different random initializations of ΠGDM. Summarizing, our approach offers an accurate, compact alternative for spectral recovery on resource-constrained platforms.