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Recently, the example-based single image spectral reconstruction from RGBimages task, aka, spectral super-resolution was approached by means of deeplearning by Galliani et al. The proposed very deep convolutional neural network(CNN) achieved superior performance on recent large benchmarks. However,Aeschbacher et al showed that comparable performance can be achieved by shallowlearning method based on A+, a method introduced for image super-resolution byTimofte et al. In this paper, we propose a moderately deep CNN model andsubstantially improve the reported performance on three spectral reconstructionstandard benchmarks: ICVL, CAVE, and NUS.

An efficient CNN for spectral reconstruction from RGB images