  To reconstruct spectral signals from multi-channel observations, in
particular trichromatic RGBs, has recently emerged as a promising alternative
to traditional scanning-based spectral imager. It has been proven that the
reconstruction accuracy relies heavily on the spectral response of the RGB
camera in use. To improve accuracy, data-driven algorithms have been proposed
to retrieve the best response curves of existing RGB cameras, or even to design
brand new three-channel response curves. Instead, this paper explores the
filter-array based color imaging mechanism of existing RGB cameras, and
proposes to design the IR-cut filter properly for improved spectral recovery,
which stands out as an in-between solution with better trade-off between
reconstruction accuracy and implementation complexity. We further propose a
deep learning based spectral reconstruction method, which allows to recover the
illumination spectrum as well. Experiment results with both synthetic and real
images under daylight illumination have shown the benefits of our IR-cut filter
tuning method and our illumination-aware spectral reconstruction method.
