  A non-parametric interpretable texture synthesis method, called the NITES
method, is proposed in this work. Although automatic synthesis of visually
pleasant texture can be achieved by deep neural networks nowadays, the
associated generation models are mathematically intractable and their training
demands higher computational cost. NITES offers a new texture synthesis
solution to address these shortcomings. NITES is mathematically transparent and
efficient in training and inference. The input is a single exemplary texture
image. The NITES method crops out patches from the input and analyzes the
statistical properties of these texture patches to obtain their joint
spatial-spectral representations. Then, the probabilistic distributions of
samples in the joint spatial-spectral spaces are characterized. Finally,
numerous texture images that are visually similar to the exemplary texture
image can be generated automatically. Experimental results are provided to show
the superior quality of generated texture images and efficiency of the proposed
NITES method in terms of both training and inference time.
