Design and Implementation of Painting Style Rendering System Based on Deep Learning

Abstract

The art of painting has always been a fan of many ordinary people. Since ancient times, many artists have devoted their lives to painting, and many masters have formed their own unique painting style. In recent years, with the rapid development of computer technology, especially the rapid development of deep learning technology, we can apply convolution neural network (cnnn) to the “book of songs” to extract and simulate the painting styles of various famous artists. Even ordinary people can explore the mystery of artistic style and create works with master artistic style.This style rendering technique is called style migration. The representation of the image in the computer is composed of pixels, which constitute a two-dimensional matrix. With the proposal of convolution neural network, different filters can be used to extract the features of the image pixel matrix, so as to obtain the painting style texture we want.