低算力深度学习下的图像卡通风格化研究

Cartoon style transfer of images with low performance deep learning

在深入研究图像风格迁移的基础上,提出了一种适用于图形处理器性能受限情况下,卡通(cartoon)图像风格迁移训练的生成式对抗网络(generative adversarial networks,GAN).利用视觉几何组(visual geometry group,VGG)网络提取图片先验信息,实现学习过程的加速;裁剪cartoonGAN模型,在保证效果的基础上,使得低性能计算条件下的网络收敛成为可能;设计合理的损失函数,保证整体风格化效果.基于tensorflow 2.0构建试验平台,通过对试验结果分析可发现,该方法的迁移效果好,稳定性强,且收敛时间短.对算法的参数和初始化方法给出了相关讨论,并提出了进一步的解决方案.

The technology of image style transfer has become increasingly important in computer vision,having achieved amazing results.However,since cartoon style is different from most art styles,there is still ample room for further improvements.This paper reviews relevant background,highlights significance of image style transfer,then focuses on cartoonGAN with limited GPU performance.Image priori information was extracted from VGG network to accelerate the learning process.CartoonGAN model was tailored to make convergence possible under low performance computing conditions due to guaranteed results.Reasonable loss function was designed to ensure overall styling effect.The network was implemented under open source deep learning framework tensorflow 2.0.Data analysis and possible improvement methods are presented.