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This paper presents a significant improvement for the synthesis of textureimages using convolutional neural networks (CNNs), making use of constraints onthe Fourier spectrum of the results. More precisely, the texture synthesis isregarded as a constrained optimization problem, with constraints conditioningboth the Fourier spectrum and statistical features learned by CNNs. In contrastwith existing methods, the presented method inherits from previous CNNapproaches the ability to depict local structures and fine scale details, andat the same time yields coherent large scale structures, even in the case ofquasi-periodic images. This is done at no extra computational cost. Synthesisexperiments on various images show a clear improvement compared to a recentstate-of-the art method relying on CNN constraints only.

Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints