Optical Codification Design in Compressive Spectral Imaging: From Mathematical to Deep Learning Optimization
Laura Galvis, Henry Arguello
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Información básica
Volumen
V55 - N1 / 2022 Especial: La Óptica en Colombia
Referencia
51083
DOI
http://dx.doi.org/10.7149/OPA.55.1.51083
Idioma
English
Etiquetas
Compressive Imaging; Optical Codification; Compressive Sensing; Spectral Imaging, deep learning
Resumen
The optical codification in compressive spectral imaging has been an area of continuous improvement. The study of the coded apertures as modulation devices and their contribution to the final spectral image quality have allowed proposing different methods of coded aperture optimization. Four different coded aperture design approaches developed through the years are reported to show the improvement process, followed by the comparison of the resulting designs in the reconstruction of spectral images. Furthermore, the latest research on deep learning-based methods has resulted in additional tools for the designs, opening more opportunities in this area of research.