Abstract
Aiming at the problem of local style migration distortion in image stylization, an improved image style migration algorithm based on deep learning network is proposed. Firstly, the VGGNet-19 network is used to extract the convolution layer features of images. Then the characteristics of convolution layer are analyzed, and the combination of style and content features is studied. The Block3 layer with the smallest content loss and style loss is selected for feature fusion with conv1_1, conv2_1, conv3_1, conv4_1 and conv5_1 layers. Finally, under the optimal combination of features, Adam algorithm is used to optimize the image style migration. The experimental results show that the proposed algorithm can effectively improve the distortion of local style migration and provide theoretical support for the implementation of style migration technology.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: DEStech Transactions on Computer Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
