Multidimensional Compressed Sensing for Spectral Light Field Imaging
<|reference_start|>Multidimensional Compressed Sensing for Spectral Light Field Imaging: This paper considers a compressive multi-spectral light field camera model that utilizes a one-hot spectralcoded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor. We propose a model that employs compressed sensing techniques to reconstruct the complete multi-spectral light field from undersampled measurements. Unlike previous work where a light field is vectorized to a 1D signal, our method employs a 5D basis and a novel 5D measurement model, hence, matching the intrinsic dimensionality of multispectral light fields. We mathematically and empirically show the equivalence of 5D and 1D sensing models, and most importantly that the 5D framework achieves orders of magnitude faster reconstruction while requiring a small fraction of the memory. Moreover, our new multidimensional sensing model opens new research directions for designing efficient visual data acquisition algorithms and hardware.<|reference_end|>
@article{cao2024multidimensional,
    title={Multidimensional Compressed Sensing for Spectral Light Field Imaging},
    author={Wen Cao, Ehsan Miandji and Jonas Unger},
    journal={In Proceedings of the 19th International Joint Conference on
  Computer Vision, Imaging and Computer Graphics Theory and Applications -
  Volume 4: VISAPP 2024, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 349-356},
    year={2024},
    doi={10.5220/0012431300003660},
    archivePrefix={arXiv},
    eprint={2405.00027},
    primaryClass={cs.CV cs.GR cs.LG eess.IV}
}