融合视觉注意机制的图像显著性区域风格迁移方法

An image saliency area style transfer method combining visual attention mechanism

对图像局部进行风格迁移通常会导致风格溢出和较小的区域风格化后效果不明显,针对该问题,提出一种图像显著性区域风格迁移方法。首先,根据人眼视觉注意机制的特点,对训练图像数据集中的显著性区域进行标注,采用快速语义分割模型进行训练,得出包含图像显著性区域的二值掩码图。然后,通过精简快速神经风格迁移模型网络层结构,并在生成网络部分采用实例正则化层,得出更具真实感的整体风格迁移结果。最后,将由语义分割得到的二值掩码图和整体风格迁移图相融合,输出最终的结果图像。在Cityscapes数据集和Microsoft COCO 2017数据集上设计了对比实验,结果显示,该方法对图像中的局部目标区域进行了均匀、细腻的风格化,且与背景区域能很好地融合在一起,实现更具真实感的风格迁移效果的同时,运行效率更占优势。

Performing style transfer on part of an image usually results in style overflow and insigni-ficant effects after stylization of smaller areas.Aiming at this problem,a style transfer algorithm for image saliency regions is proposed.Firstly,according to the characteristics of the human visual attention mechanism,the saliency regions in the training image data set are labeled,and the fast semantic segmentation model is used for training to obtain a binary mask image containing the saliency regions of the image.Then,by simplifying the network layer structure of the fast neural style transfer model,and adopting the instance regularization layer in the generating network part,a more realistic overall style transfer result is obtained.Finally,the binary mask image obtained by semantic segmentation is combined with the overall style transfer image,and the final result image is output.A comparative experiment was carried out on the Cityscapes dataset and the Microsoft COCO 2017 dataset.The results show that the local target area in the image is stylized uniformly and delicately,and can be well integrated with the background area.While a more realistic style transfer effect is achieved,operating efficiency is more dominant.