Multispectral Style Distances and Application to Texture Synthesis Using RGB Convolutional Neural Networks

State-of-the-art methods for RGB texture synthesis and style transfer leverage the representations learned by convolutional neural networks (CNN) on large datasets. Style distances, obtained by comparing statistics of deep features, play a pivotal role in synthesis procedures. Extending these distances to multispectral images is challenging because the pre-trained CNN only operate on RGB images. This work presents two multispectral style distances that still rely on a RGB CNN to avoid additional training. The first consists in a classical style distance, averaged over images formed by triplets of spectral bands. The second takes advantage of a projection of the multispectral pixels onto a three-dimensional space. We demonstrate their efficiency by performing multispectral texture synthesis.