Research on the Synthetic Method of Ink Painting Based on Convolutional Neural Network

In this paper, we defined a characteristic response of convolutional layer mapping in a convolutional neural network model, and explored correlation between them by adjusting a trained convolutional neural network model's structure. First of all, we did pre-processing include contrastive enhancement for photos. Then matched the photo's characteristic response to obtain its content information in a random image. And matched correlation between the characteristic responses of ink painting again to obtain its style information. The final step was to synthesize the image. Traditional ink painting method usually generate images with some basic features. So a particular style can't be assigned, even generating stiff images without artistic conception. Aim at these situations, this paper proposes an ink painting synthesis methods based on convolutional neural network, which can produce better images. It retains both outline information of original photo and overall texture information of ink painting. This paper also presents a method, which can merge ink painting style into a photo. The method works well in synthesizing grayscale image such as ink painting.