Here, we present a pixelwise illuminant recovery method for both, trichromatic and multi or hyperspectral images which employs a convolutional neural nettwork. The network used here is based upon the simple, yet effective architecture employed by the CIFARIO-quick net[1]. The network is trained using a loss function which employs the angular difference between the target illuminant and the estimated one as the data term. The loss used here also includes a regularisation term which encourages smoothness in the spectral domain. Moreover, the network takes, at input, a tensor which is constructed making use of an image patch at different scales. This allows the network to predict the illuminant per-pixel using locally supported multiscale information. We illustrate the utility of our method for both, colour and hyperspectal illuminant recovery and compare our results against other techniques elsewhere in literature.