Dual-domain style transfer network
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a Dual-Domain Style Transfer Network to reduce artifacts in arbitrary style transfer. The novel approach enhances style semantics and global textures for improved image quality.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background
- Arbitrary style transfer is gaining traction for its diverse applications.
- Current methods often result in artifacts and poor textures due to limited semantic understanding and long-range dependency capture.
Purpose Of The Study
- To develop an advanced arbitrary style transfer method that minimizes artifacts and enhances texture quality.
- To address the limitations of existing approaches by exploring style semantic distribution and long-range dependencies.
Main Methods
- Introduced a Dual-Domain Style Transfer Network.
- Incorporated Adaptive Normalization with Style Semantics Awareness using self-attention.
- Implemented Global Style Texture Enhancement in the frequency domain.
Main Results
- Achieved state-of-the-art performance on MSCOCO and Wikiart datasets.
- Secured top scores in Learned Perceptual Image Patch Similarity (0.616), Structural Similarity Index (0.467), and content loss (2.31).
- Obtained the second-best score in style loss (3.08).
Conclusions
- The proposed Dual-Domain Style Transfer Network effectively reduces artifacts and improves texture quality.
- The integration of style semantics awareness and frequency domain enhancement offers a significant advancement in arbitrary style transfer.
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