Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond
This work addresses the problem of heterogeneous encoding design for researchers in computational spectral imaging, offering a framework for fair comparison and optimization, though it is incremental as it builds on existing encoding types.
The paper tackled the challenge of fairly comparing amplitude, phase, and wavelength encoding systems in computational spectral imaging by proposing a unified encoding model (UEM) that covers all physical systems, and extended it to ideal versions to explore their full potential, resulting in a holistic comparison that provides insights for future system design.
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.