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MDPI Sensors - Special Issue "Advances in Spectroscopy and Spectral Imaging"

25 Septembre 2020


Catégorie : Publication


Dear colleagues,

 

We are editing a special issue (MDPI Sensors journal) named "Advances in Spectroscopy and Spectral Imaging":

www.mdpi.com/journal/sensors/special_issues/Spectroscopy_Spectral_Imaging

 

Best wishes,

Pierre-Jean Lapray, Yusuke Monno, and Jean-Baptiste Thomas.

 

 

Dear colleagues,

 

We are editing a special issue (MDPI Sensors journal) named "Advances in Spectroscopy and Spectral Imaging":

www.mdpi.com/journal/sensors/special_issues/Spectroscopy_Spectral_Imaging

You can see a summary below.

 

You are welcome to contribute with your good work over the coming year.

Best wishes,

Pierre-Jean Lapray, Yusuke Monno, and Jean-Baptiste Thomas.

 

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Spectroscopy aims at recovering the spectral signature of light at a scene point, within a given spectral range and a given spectral resolution. Spectral imaging enhances this functionality by adding spatial dimension, leading to a spatiospectral data representation (i.e., a spectral data cube). On one hand, novel hardware designs dedicated to spectroscopy and spectral imaging (SSI) are demanded to improve the efficiency, flexibility, or compactness of the SSI systems. On the other hand, dedicated data processing is required for the emergence of SSI systems.

Recent advances in the field could potentially lead to the massification of SSI, and a better implication of SSI in applications, such as for computer vision, computer graphics, or remote sensing. To further help SSIs to break through into applications, it is necessary to go beyond our understanding of their limitations.

This Special Issue focuses on these topics, so the different issues, achievements, and progress from different disciplines are available from one single issue.

Potential topics include but are not limited to:

  • Technology: spectral sensors, optical design, camera design, acquisition setup, etc.
  • Computational algorithm: imaging model, data processing, noise reduction, calibration, image enhancement, demosaicing, super-resolution, high dynamic range, etc.
  • Inverse problem: spectral reconstruction, illuminant estimation, reflection mode separation, rendering, matching, etc.
  • Data mining for spectral information: learning, CNN, time series, etc.
  • Applications in computer vision: medical imaging, automotive, cultural heritage (classification, text analysis), etc.
  • Applications in computer graphics: cultural heritage (visual reproduction), etc.
  • Other SSI applications in remote sensing, chemistry, biology, etc.