In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach.
翻译:在本文中,我们提出一个光现实风格传输网络,强调光现实图像的自然效应。一般来说,图像内容的扭曲和缺乏细节是风格传输字段的两个典型问题。为此,我们设计了一个新框架,使用U-Net结构来维持丰富的空间线索,采用多层特征聚合(MFA)方法同时提供在Styliz化处理中浅层获得的细节。特别是,基于稠密块的编码器和形成对称的U-Net结构的解密器,共同涉及实现有效的特征提取和图像重建。此外,一个基于MFA和“适应实例正常化”(AdaIN)的传输模块被插入了连接跳过的位置,以实现质化。因此,Styl化图像拥有真实照片的纹理,保存丰富的内容细节,而没有引入任何遮罩或后处理步骤。公共数据集的实验结果表明,我们的方法在结构上实现了更忠实的相似性更低风格的损失,反映了我们的方法的有效性和优点。