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A comparative study of methods and algorithms for spatially resolved spectral value reconstruction with multispectral resolving filter-on-chip CMOS sensors

: Dittrich, P.-G.; Radtke, L.; Zhang, C.; Guo, S.; Buch, B.; Rosenberger, M.; Notni, G.


Witzigmann, B. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Physics and Simulation of Optoelectronic Devices XXVII : 5-7 February 2019, San Francisco, California, United States
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 10912)
ISBN: 978-1-5106-2466-5
ISBN: 978-1-5106-2467-2
Paper 109120Z, 10 S.
Conference "Physics and Simulation of Optoelectronic Devices" <27, 2019, San Francisco/Calif.>
Conference "Optoelectronics, Photonic Materials and Devices" <2019, San Francisco/Calif.>
Fraunhofer IOF ()

Mosaic filter-on-chip CMOS sensors enable the parallel acquisition of spatial and spectral information. These mosaic sensors are characterized by spectral filters which are applied directly on the sensor pixel in a matrix which is multiplied in the x- and y-direction over the entire sensor surface. Current mosaic sensors for the visible wavelength area using 9 or 16 different spectral filters in 3 × 3 or 4 × 4 matrices. Methods for the reconstruction of spectral reflectance from multispectral resolving sensors have been developed. It is known that the spectral reflectance of natural objects can be approximated with a limited number of spectral base functions. Therefore, continuous spectral distributions can be reconstructed from multispectral data of a limited number of channels. This paper shows how continuous spectral distributions can be reconstructed using spectral reconstruction methods like Moore-Penrose pseudo-inverse, Wiener estimation, Polynomial reconstruction and Reverse principal component analysis. These methods will be evaluated with monolithic mosaic sensors. The Goodness of Fit Coefficient and the CIE color difference are used to evaluate the reconstruction results. The reconstruction methods and the spectral base functions applied for the mosaic sensors are juxtaposed and practical conclusions are drawn for their application.