基于深度学习的纺织品图案智能辅助设计

Intelligent Aided Design of Textile Patterns Based on Deep Learning

为了实现基于深度学习的纺织品图案智能辅助设计系统,将分形图案与深度学习风格迁移算法相结合,利用深度学习框架Tensorflow,首先生成基于Mandelbrot集合的分形图案,接着从图像转换网络结构、感知损失函数等几个方面改进了已有的神经网络风格迁移算法,对分形图案进行风格化设计,使生成的创意图案更符合纺织品图案边界要求高、保持图案细节完整的特点,更加符合纺织品打印要求。

In order to realize the intelligent aided design system of textile pattern based on deep learning,the fractal pattern is combined with the algorithm of deep learning style transfer.Using Tensorflow,the fractal pattern based on Mandelbrot set is generated firstly.Then,the existing neural network style transfer algorithm is improved from the aspects of image conversion network structure and perceptual loss function,the fractal pattern is stylized to make the creative pattern meet the requirements of textile pattern boundary,keep the pattern details complete and meet the textile printing requirements.