Dyeing creation: a textile pattern discovery and fabric image generation method

Creating different textile patterns to generate printable fabric images is a difficult image processing task. To accomplish this task, we propose a novel framework for dyeing creation, which allows non-professionals to design individual fabric images. The two main components of this framework are textile pattern discovery and fabric image generation. Since the objects in the fabric image are multi-category and multi-scale, we employ a combination of object pattern and template pattern to discover the repetitive pattern, which can better extract objects and analyze spatial structure. However, the image created with objects and templates cannot be dyed directly, because it does not meet the physical size requirements of dyeing. Therefore, we propose an image super-resolution method for fabric image generation based on edge information prior. It solves the high magnification problem of single image by using deep neural network without training data sets. Extensive experiments on fabric images demonstrate that the proposed algorithm achieves good results both qualitatively and quantitatively. Our method has comparable accuracy compared with state-of-the-art methods and visual results demonstrate our superiority in restoring edges while generating fabric images.