Design And Implementation Of Local Style Transfer Method Based On Salient Object Detection

In the era of diversification,people's demand for multimedia information processing is increasing.They no longer only focus on image smoothing,enhancement and other processing forms,but pursue personalized and artistic image creation experience.Image style transfer technology can transform one image style to another content image,which is a research hotspot in the field of image processing.However,the current image style transfer technology mainly takes the whole image as the transfer object,which can not meet the user's changeable creation methods.Based on the existing deep learning theory,this paper studies a local style transfer method based on salient object detection to enrich the diversity of image style transfer.The main work of this paper includes:(1)The image style transfer method in this paper uses CycleGAN network.Aiming at the problems of low quality stylized image generated by original CycleGAN network and unstable model training,the network discriminator and generator are improved respectively.In the discriminator part,a spectral normalization method is used to limit the spectral norm of the weight matrix of each convolution layer in the discriminator,so that the discriminator function satisfies Lipschitz constraint,so as to stabilize the training of the discriminator model.In the generator part,this paper redesigns a generator architecture based on the original design idea of CycleGAN network generator and a new adaptive layer instance,normalization layer.At the same time,this paper studies the influence of the number of residual blocks and the upsampling method on the results of the generator,and in order to reduce the training time of the network,we observe the data set and make reasonable manual selection.(2)The salient object detection method in this paper uses F3net network.When F3net network is used for feature extraction,the deep residual network can not capture enough context information,resulting in the loss of spatial resolution.In this paper,PyConvResNet network composed of pyramid convolution is used as the feature extraction network of encoder.At the same time,in order to further expand the receptive field,better capture the global contrast information,this paper also designs a new receptive field module.(3)Aiming at the hard boundary problem of local content and style when foreground and background are stylized separately,this paper studies a local style transfer method based on improved Poisson fusion.Firstly,the stylized image is taken as the target image of the improved Poisson fusion algorithm,and the original natural image is taken as the source image.The salient object detection model is used to generate the binary mask of the foreground of the source image.Then,the target image,the source image and the binary mask are fused in three steps,and finally the local style transfer is realized.(4)This paper designs a local style transfer system based on salient object detection.Users only need to import the required content image,and then select the desired style type and transfer area.The system will automatically carry out global or local stylization according to the selected content image to meet the needs of user's personalized creation.