NDLI: GLStyleNet: exquisite style transfer combining global and local pyramid features

Content Provider	IET Digital Library
Author	Wang, Zhizhong Zhao, Lei Lin, Sihuan Mo, Qihang Zhang, Huiming Xing, Wei Lu, Dongming
Abstract	Recent studies using deep neural networks have shown remarkable success in style transfer, especially for artistic and photo-realistic images. However, these methods cannot solve more sophisticated problems. The approaches using global statistics fail to capture small, intricate textures and maintain correct texture scales of the artworks, and the others based on local patches are defective on global effect. To address these issues, this study presents a unified model [global and local style network (GLStyleNet)] to achieve exquisite style transfer with higher quality. Specifically, a simple yet effective perceptual loss is proposed to consider the information of global semantic-level structure, local patch-level style, and global channel-level effect at the same time. This could help transfer not just large-scale, obvious style cues but also subtle, exquisite ones, and dramatically improve the quality of style transfer. Besides, the authors introduce a novel deep pyramid feature fusion module to provide a more flexible style expression and a more efficient transfer process. This could help retain both high-frequency pixel information and low-frequency construct information. They demonstrate the effectiveness and superiority of their approach on numerous style transfer tasks, especially the Chinese ancient painting style transfer. Experimental results indicate that their unified approach improves image style transfer quality over previous state-of-the-art methods.
Starting Page	575
Ending Page	586
Page Count	12
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 8, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0844
Journal	IET Computer Vision
Publisher Date	2020-10-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Art Chinese Ancient Painting Style Transfer Computer Vision And Image Processing Technique Deep Neural Network Deep Pyramid Feature Fusion Module Exquisite Style Transfer Feature Extraction Flexible Style Expression Global Channel-level Effect Global Pyramid Feature Global Semantic-level Structure GLStyleNet Graphics Technique High-frequency Pixel Information Humanities Computing Image Colour Analysis Image Fusion Image Representation Image Style Image Texture Intricate Textures Local Patch-level Style Local Patches Local Pyramid Feature Low-frequency Construct Information Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Photo-realistic Image Realistic Image
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-scale deep neural network for salient object detection

Adaptive learning feature pyramid for object detection

Dynamic gesture recognition based on feature fusion network and variant ConvLSTM

Patch strategy for deep face recognition

OCPAN: multi-person pose estimation with more attention on residual information

Multi-scale object detection by bottom-up feature pyramid network

GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features

Classification of textured images based on new information fusion methods

Unconstrained ear recognition using deep neural networks

GLStyleNet: exquisite style transfer combining global and local pyramid features

Similar Documents

Multi-scale deep neural network for salient object detection

Adaptive learning feature pyramid for object detection

Dynamic gesture recognition based on feature fusion network and variant ConvLSTM

Patch strategy for deep face recognition

OCPAN: multi-person pose estimation with more attention on residual information

Multi-scale object detection by bottom-up feature pyramid network

GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features

Classification of textured images based on new information fusion methods

Unconstrained ear recognition using deep neural networks

GLStyleNet: exquisite style transfer combining global and local pyramid features