NDLI: F2PNet: font-to-painting translation by adversarial learning

Content Provider	IET Digital Library
Author	Li, Guanzhao Zhang, Jianwei Chen, Danni
Abstract	For Chinese font images, when all their strokes are replaced by pattern elements such as flowers and birds, they become flower–bird character paintings, which are traditional Chinese art treasures. The generation of flower–bird painting requires professional painters’ great efforts. How to automatically generate these paintings from font images? There is a huge gap between the font domain and the painting domain. Although many image-to-image translation frameworks have been proposed, they are unable to handle this situation effectively. In this study, a novel method called font-to-painting network (F2PNet) is proposed for font-to-painting translation. Specifically, an encoder equipped with dilated convolutions extracts features of the font image, and then the features are fed into the domain translation module for mapping the font feature space to the painting feature space. The acquired features are further adjusted by the refinement module and utilised by the decoder to obtain the target painting. The authors apply adversarial loss and cycle-consistency loss to F2PNet and further propose a loss term, which is called recognisability loss and makes the generated painting have font-level recognisability. It is proved by experiments that F2PNet is effective and can be used as an unsupervised image-to-image translation framework to solve more image translation tasks.
Starting Page	3243
Ending Page	3253
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0476
Journal	IET Image Processing
Publisher Date	2020-08-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adversarial Loss Art Chinese Font Image Computer Vision And Image Processing Technique Cycle-consistency Loss Domain Translation Module F2PNet Feature Extraction Flower–bird Character Paintings Font Domain Font Feature Space Font Image Font-level Recognisability Font-to-painting Network Font-to-painting Translation Generated Painting Humanities Computing Image And Video Coding Image Coding Image Recognition Image Translation Task Image-to-image Translation Frameworks Knowledge Engineering Technique Painting Painting Domain Painting Feature Space Target Painting Unsupervised Image-to-image Translation Framework Unsupervised Learning
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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