@InProceedings{8553391,
  author = {C. V. Correa and H. Arguello and G. R. Arce},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Multi-Resolution Reconstructions from Compressive Spectral Coded Projections},
  year = {2018},
  pages = {1995-1999},
  abstract = {Compressive spectral coded projections are attained by an imaging detector as a spatial-spectral field traverses diverse optical elements such as a coded aperture and a dispersive element. Compressed sensing reconstruction algorithms are used to recover the underlying data cube at the resolution enabled by the captured projections. Such reconstructions, however, are computationally expensive because of the data dimensions. In this paper, a multi-resolution (MR) reconstruction approach is presented, such that several versions of the data cube can be recovered at different spatial resolutions, by employing gradient intensity maps. Simulations show that this approach overcomes interpolation results in up to 3dB of PSNR in noisy scenarios.},
  keywords = {compressed sensing;image reconstruction;image resolution;interpolation;multiresolution reconstruction approach;data cube;multiresolution reconstructions;compressive spectral coded projections;spatial-spectral field traverses diverse optical elements;coded aperture;dispersive element;reconstruction algorithms;captured projections;spatial resolutions;PSNR;Image reconstruction;Spatial resolution;Optical imaging;Optical sensors;Compressive spectral imaging;Multi-resolution;Spectral Imaging;Compressed sensing},
  doi = {10.23919/EUSIPCO.2018.8553391},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437352.pdf},
}

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Compressed sensing reconstruction algorithms are used to recover the underlying data cube at the resolution enabled by the captured projections. Such reconstructions, however, are computationally expensive because of the data dimensions. In this paper, a multi-resolution (MR) reconstruction approach is presented, such that several versions of the data cube can be recovered at different spatial resolutions, by employing gradient intensity maps. 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