NITES: A Non-Parametric Interpretable Texture Synthesis Method
This addresses the need for mathematically transparent and efficient texture synthesis for applications in computer graphics and vision, though it appears incremental as it builds on existing non-parametric approaches.
The paper tackles the problem of texture synthesis by proposing NITES, a non-parametric interpretable method that generates visually similar textures from a single input image, offering superior quality and efficiency with reduced computational cost compared to deep neural networks.
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.