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.

鉴于训练基于生成对抗网络的图像油画风格迁移和重构算法的难度，并且消除了生成器和鉴别器的损失梯度，提出了一种基于梯度惩罚的改进生成对抗网络，并构造总方差损失函数进行图像油画风格迁移和重构的研究。首先，将Wasserstein距离（WGAN）添加到生成对抗网络的损失函数中，以提高替代迭代训练的稳定性。其次，在损失函数中增加梯度惩罚（WGAN-GP），以解决训练中梯度消失的问题。最后，在CycleGAN Loss噪声约束的基础上，介绍了LBP的纹理特征和原型的整体变化，以改善油画风格迁移后图像的边缘和纹理强度。实验结果表明，本文构造的WGAN-GP算法具有稳定的梯度和交替迭代收敛的能力，总变化损失噪声约束可以为图像油画风格的迁移过程提供良好的边缘和纹理细节。与现有的主流算法相比，本文提出的算法具有更好的图像油画风格迁移和重构性能，并具有较好的图像油画风格迁移和重构效果。