Computational Spectral Imaging with Unified Encoding Model and Beyond

Xinyuan Liu, Lingen Li, Lin Zhu, Lizhi Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1368-1378

Computational spectral imaging is drawing increasing attention owing to the snapshot advantage and amplitude phase and wavelength encoding systems are three types of representative implementations. Fairly comparing and understanding the performance of these systems is essential but challenging due to the heterogeneity in encoding design. To overcome this limitation we propose the unified encoding model (UEM) that covers all physical systems using the three encoding types. Specifically the UEM comprises physical amplitude physical phase and physical wavelength encoding models that can be combined with a digital decoding model in a joint encoder-decoder optimization framework to compare the three systems under a unified experimental setup fairly. Furthermore we extend the UEMs to ideal versions namely ideal amplitude ideal phase and ideal wavelength encoding models which are free from physical constraints to explore the full potential of the three types of computational spectral imaging systems. Finally we conduct a holistic comparison of the three types of computational spectral imaging systems and provide valuable insights for designing and exploiting these systems in the future.