Spectral recovery from measured camera signals based on deep learning lead to significant advancements of the potential reconstruction quality. However, most deep learning based approaches only consider RGB cameras and are targeting object classification in particular or remote sensing in general as their final application. Within this work, we analyze the influence of a joint filter optimization and spectral recovery for multi-spectral image acquisition with the underlying goal of capturing high-fidelity color images. An evaluation on the influence of the total camera channel count on the reproduction quality is provided. Finally, a possible normalization of spectral data is discussed.