
This paper presents a comprehensive experimental evaluation of spectral reconstruction methods in multispectral imaging systems, focusing on two multispectral camera technologies with differing spectral characteristics: spectral filter array and filter wheel. These systems were assessed under a controlled LED-based illumination setup. A range of reconstruction methods, encompassing both model-based and training-based approaches, were analyzed in their baseline forms as well as in adaptive configurations, which select optimal local training subsets based on spectral reflectance or camera response similarity. Experiments were conducted using a custom-built imaging setup and two well-characterized spectral reflectance datasets: the standard Munsell and the Munsell Student Color sets.
Results demonstrate that training-based methods significantly outperform model-based methods in both spectral and colorimetric accuracy. Adaptive dataset selection further enhances performance in many cases, particularly for the SpectroCam filter wheel camera. The influence of illumination on reconstruction accuracy is also examined, revealing that model-based methods are especially sensitive to the spectral power distribution of the light source. These findings offer practical and technical guidance for the design and calibration of multispectral imaging systems aimed at achieving high-accuracy spectral recovery.

Multispectral images contain more spectral information of the scene objects compared to color images. The captured information of the scene reflectance is affected by several capture conditions, of which the scene illuminant is dominant. In this work, we implemented an imaging pipeline for a spectral filter array camera, where the focus is the estimation of the scene reflectances when the scene illuminant is unknown. We simulate three scenarios for reflectance estimation from multispectral images, and we evaluate the estimation accuracy on real captured data. We evaluate two camera model-based reflectance estimation methods that use a Wiener filter, and two other linear regression models for reflectance estimation that do not require an image formation model of the camera. Regarding the model-based approaches, we propose to use an estimate for the illuminant's spectral power distribution. The results show that our proposed approach stabilizes and marginally improves the estimation accuracy over the method that estimates the illuminant in the sensor space only. The results also provide a comparison of reflectance estimation using common approaches that are suited for different realistic scenarios.