基于循环生成对抗网络的图像风格迁移

图像风格迁移是指将学习到的油画图像风格应用到其他图像上,让图像拥有油画的风格,当前生成对抗网络已被广泛应用到图像风格迁移中。针对循环生成对抗网络CycleGAN在处理图像时纹理清晰度不高的问题,提出了加入局部二值模式LBP算法的方法,将LBP算法加入生成对抗网络的生成器中,增强了循环对抗生成网络提取图像纹理特征内容的效果。针对生成图像产生噪声的问题,在损失函数中加入Total Variation Loss来约束噪声。实验结果表明,循环生成对抗网络加入LBP算法和Total Variation Loss后能提高生成图像的质量,使之具有更好的视觉效果。

Image style migration based on cycle generative adversarial networks

Image style migration refers to learning the style of oil painting pictures and applying the learned style to other pictures to make the pictures have the style of oil painting. The current methods based on generative adversarial networks have been widely used in image style migration. Aiming at the problem that Cycle Generative Adversarial Networks (CycleGAN) do not have high texture definition when processing images, a method of adding a Local Binary Pattern (LBP) algorithm is proposed. The LBP algorithm is added into the generation model of CycleGAN to enhance the extraction of image texture features by CycleGAN. Aiming at the problem of noise in the generated images, Total Variation Loss is added into the loss function to constrain the noise. The experimental results show that the quality of the generated images can be improved by adding LBP algorithm and Total Variation Loss, and the generated images have better visual effects.