Abstract. Hyperspectral image (HSI) recovery from a single RGB image has attracted much attention, whose performance has recently been
shown to be sensitive to the camera spectral sensitivity (CSS). In this
paper, we present an efficient convolutional neural network (CNN) based
method, which can jointly select the optimal CSS from a candidate
dataset and learn a mapping to recover HSI from a single RGB image captured with this algorithmically selected camera. Given a specific
CSS, we first present a HSI recovery network, which accounts for the underlying characteristics of the HSI, including spectral nonlinear mapping
and spatial similarity. Later, we append a CSS selection layer onto the
recovery network, and the optimal CSS can thus be automatically determined from the network weights under the nonnegative sparse constraint.
Experimental results show that our HSI recovery network outperforms
state-of-the-art methods in terms of both quantitative metrics and perceptive quality, and the selection layer always returns a CSS consistent
to the best one determined by exhaustive search