Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

Abstract

This study is to explore the effect of deep learning algorithms applied to the non-photorealistic rendering (NPR) of ink painting. The powerful processing capabilities of NPR technology and convolutional neural networks (CNN) in computer vision are analyzed. Aiming at the features of ink texture images, a new sample-based ink texture synthesis method is proposed. The feature representation of ink texture is calculated using the CNN model, and then an ink texture with the same feature representation is generated on a random image. In addition, a synthesis method of ink painting picture is proposed based on the CNN. The results show that the ink texture generated by the ink texture synthesis algorithm is very good, and the Visual Geometry Group Network (VGG-Net) model shows the best generation effect. For images generated by high-level networks lack a lot of pixel information, but retain the main feature information of the image. When the learning rate is 10, the effect of the ink image synthesized based on the content image is slightly worse; and the image generated by the fusion of multiple styles confirms the feasibility of synthesizing ink images of different "sizes and styles". Therefore, this ink image synthesized on the basis of the CNN model shows a better effect, which extends the possibility of computer creation of richer ink painting works.