Application of Local Color Simulation Method of Landscape Painting Based on Deep Learning Generative Adversarial Networks

Abstract

The traditional computer simulation landscape painting has the defect that it cannot completely present the painting, the application research of local color simulation method of landscape painting based on deep learning adversarial networks is proposed. Based on the improved generative adversarial networks design, the semantic label hierarchical classification design of landscape paintings is carried out. Through the training and design of generative adversarial networks, the generator and discriminator of landscape painting are designed, and the semantic segmentation algorithm of landscape painting of generative adversarial networks is proposed. Finally, a simulation experiment test is carried out on the local color simulation of landscape painting. The results show that this application method is better than the traditional computer simulation method, it can fully reflect the realism of landscape art painting and the integrity of the picture itself. The texture detail clarity of the generated map is stronger than that of the traditional one, and the semantic content is more accurate. It has important practical reference value.