Application of Style Transfer Algorithm in Artistic Design Expression of Terrain Environment

Abstract

The use of artistic expression to depict and express terrain and landform can not only convey terrain information, but also spread art and culture. The existing landscape design methods focus on the accurate expression of terrain height and the realistic expression of form, but neglect the aesthetic aspect of landscape design. In view of this situation, this paper studied the use of generative adversarial network, constructed the presentation mode of landscape plane style, and realized the expression of landscape art style. A terrain style transfer model based on a pre-trained deep neural network model and style transfer algorithm was constructed to achieve a variety of terrain style expressions. The results showed that, in terms of Peak Signal to Noise Ratio, the proposed style transfer algorithm was higher than style attention network and adaptive instance normalization, and the peak signal-to-noise ratio index value was increased by 7.5% and 16.5%. This indicated that the style transfer model proposed by the topographic artistry research had more advantages in terms of image diversity and fidelity. The Structural Similarity Index of the proposed algorithm has been greatly improved. This research expands the method of computer rendering of terrain environment art, which is of great significance for the preservation of traditional Chinese culture.