TITLE:
Research on Image Generation and Style Transfer Algorithm Based on Deep Learning
AUTHORS:
Ruikun Wang
KEYWORDS:
Deep Learning, Image Generation, Style Transfer
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.9 No.8,
August
28,
2019
ABSTRACT: Aiming
at the current process of artistic creation and animation creation, there are a
lot of repeated manual operations in the process of conversion from sketch to the stylized image. This paper presented a solution based on a deep
learning framework to realize image generation and style transfer. The method
first used the conditional generation to resist the network, optimizes the loss
function of the training mapping relationship, and generated the actual image
from the input sketch. Then, by defining and optimizing the perceptual loss
function of the style transfer model, the style features are extracted from the
image, thereby forming the actual The conversion between images and stylized
art images. Experiments show that this method can greatly reduce the work of
coloring and converting with different artistic effects, and achieve the purpose of transforming simple stick
figures into actual object images.