Tunable Optimally‐Coded Snapshot Hyperspectral Imaging for Scene Adaptation

Abstract Snapshot hyperspectral imaging (SHI) is increasing demand for various applications in dynamic scenes. Current mainstream solutions rely on machine learning with open‐source datasets to acquire fixed compression encoder and reconstruction decoder, which limits their generalizability across diverse real‐world scenarios. Herein, these challenges are addressed by a tunable optimally‐coded SHI (TOSHI) system that allows dynamic optimization of optical encoding elements and software decoding strategies based on actual scene data. To improve scene adaptability, a domain‐aware adaptive mechanism is introduced that extracts spatial and spectral features from ground truth data to calibrate the system through transfer learning and parameter‐conserving fine‐tuning. Leveraging spatial division multiplexing technology, TOSHI is equipped with an auxiliary imaging structure to acquire ground truth, enabling more efficient scene adaptation. As a demonstration, a proof‐of‐concept prototype is developed with an image resolution of up to 5120 × 5120 pixels, an angular resolution of 0.05 degrees, a spectral resolution of 10 nm within the visible wavelength, and a spatial‐temporal resolution of up to 2048 × 2048 pixels @14.7fps, achieving a PSNR improvement of ≈3.54 dB over conventional SHI systems. Additionally, TOSHI has been verified for online industrial measurements, including active and passive lighting devices, through extensive experiments.