基于改进CycleGAN的图像风格迁移

Image style transfer based on improved CycleGAN

图像风格迁移是用风格图像对指定图像的内容进行重映射,利用GAN自动进行图像风格迁移,可减少工作量,且结果丰富。特定情况下GAN方法所用的配对数据集很难获得。为了避免利用传统GAN进行图像风格迁移受到成对数据集的限制,提高风格迁移效率,本文利用改进的循环一致性对抗网络CycleGAN实现图像风格迁移,用密集连接卷积网络DenseNet代替原来网络生成器的深度残差网络ResNet,用同一映射损失和感知损失组成的损失函数度量风格迁移损失。所做改进使网络性能得到了提升,取消了网络对成对样本的限制,提高了风格迁移生成图像的质量。同时进一步提高了稳定性,加快了网络收敛速度。论文所提方法对建筑图像进行了风格迁移,实验结果表明,生成图像的PSNR值平均提高了6.27%,SSIM值均提高了约10%。因此,本文提出的改进的CycleGAN图像风格迁移方法生成的风格图像效果更优。

Image style transfer exploits a specified style to modify given image content.An automatic image style transfer based on a Generative Adversarial Network(GAN)can reduce the workload and yield rich results.In some cases,the pair datasets required by the classical GAN were difficult to obtain.To overcome the limitations of paired datasets by a traditional GAN and improve the efficiency of style transfer,this study proposed an image style transfer method based on an improved Cycle-consistent adversarial network(CycleGAN).In this study,the deep residual network adopted by the conventional network generator was replaced by the dense connection convolution network,and a novel loss function composed of the same mapping and perceptual losses was used to measure the style transfer loss.These improvements were shown to increase the network performance,overcome the network s limitations on paired samples,and improve the quality of images generated by style migration.In addition,the stability was further improved and the network convergence speed was accelerated.Experiments demonstrate that the peak signal-to-noise ratio of the image generated by the proposed method increase 6.27%on average,where as the structural similarity index measure increased by approximately 10%.The improved CycleGAN image style transfer method proposed in this study can thus generate better style images.