Improved generative adversarial network and its application in image oil painting style transfer

摘要

In view of the difficulty in training the algorithm of image oil painting style migration and reconstruction based on the generative adversarial network, and the loss gradient of generator and discriminator disappears, this paper proposes an improved generative adversarial network based on gradient penalty, and constructs the total variance loss function to carry out the research of image oil painting style migration and reconstruction. Firstly, the Wasserstein distance (WGAN) is added to the loss function of the generative adversarial network to improve the stability of the alternative iterative training; secondly, the gradient penalty (WGAN-GP) is added to the loss function to deal with the problem of gradient disappearance in the training; finally, the LBP texture feature and total variation of the prototype are introduced based on the CycleGAN Loss noise constraint is used to improve the edge and texture strength of the image after migration of oil painting style. The experimental results show that the WGAN-GP algorithm constructed in this study has the ability of stable gradient and alternating iterative convergence, and the total variation loss noise constraint can provide good edge and texture details for the migration process of image oil painting style. Compared with the existing mainstream algorithm, the algorithm proposed in this study has better performance of image oil painting style migration and reconstruction, and better effect of image oil painting style migration and reconstruction. (C) 2020 Elsevier B.V. All rights reserved.