Multispectral resolving filter-on-chip snapshot-mosaic CMOS cameras are a convenient, reliable and affordable approach for the parallel acquisition of spatial and spectral information. The combination of pixel-arranged spectral filter matrices on CMOS sensors increases their integration density and system complexity by several times compared to standard RGB cameras. Due to the system design, these cameras have an increased spectral crosstalk and specific dependencies from the angle of illumination. To ensure the comparability and reproducibility of the measured values, arrangements, methods and algorithms for the characterization are developed and applied to characterize the capabilities of these cameras. It will be shown how to characterize these cameras in accordance to the EMVA1288 standard and which methods, algorithms and additional measurement arrangements have been developed and applied to make suggestions for extending this standard concerning a possible extension of the characterization for spectral crosstalk and angle dependencies.
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.
The Group for Quality Assurance and Industrial Image Processing has built a new experimental setup to characterize cameras and image sensors according to EMVA1288 standard. Next to the investigation of SLN (Sensitivity-Linearity- Noise) and spatial non-uniformities, the new test bench also provides an examination of the temperature dependence of sensors. The temperature dependent dark current produces an undesirable signal which affects the image quality negative and thus has to be known. It is caused by thermally induced electrons and increases linearly with exposure time as well as exponentially with temperature. To measure the dark current, it is necessary to vary and determine the temperature of the sensor. This was made possible by a climate chamber with a Peltier element, which enables a heating and cooling of the camera. An infrared sensor allows a contact-free detection of the actual camera temperature. Furthermore, the light source was improved for the new test bench. With the installed custom light source and integration sphere a homogeneous irradiation up to 97% is ensured. This way better results in tests were achieved. The light source with variable filter housing enables the use of monochromatically light in a wavelength range of 350 – 1700nm. A live monitoring of the irradiation during the image capturing is possible. A MATLAB script assists in the configuration of the camera, the measurement and the data storage. The user is guided step by step through the program. At the end of the measurements an automated evaluation follows, which illustrates graphs and parameters in a streamlined and print-ready format.