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Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond

arXiv 2312.13310 ↗First posted 20 Dec 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 74 references that are in the map. First posted on arXiv 20 Dec 2023.

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19 of 74 references.

References · 19

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7 more
  1. 2013Coded aperture compressive temporal imagingcited by 57
  2. 2020Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Opticscited by 39
  3. 2021HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imagingcited by 26
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  5. 2022A Hyperspectral and RGB Dataset for Building Facade Segmentationcited by 7
  6. 2022Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoderscited by 6
  7. 2020Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstructioncited by 2

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