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Extending user control for image stylization using hierarchical style transfer networks.

Sunder Ali Khowaja¹, Sultan Almakdi², Muhammad Ali Memon³

¹Department of Telecommunication, Faculty of Eng. And Tech, University of Sindh, Jamshoro, Sindh, 76090, Pakistan.

|December 13, 2024

Summary

This summary is machine-generated.

This study introduces the Hierarchical Style Transfer Network (HSTN) for image stylization, offering users control over style intensity. HSTN enhances detail preservation and style fusion, outperforming existing methods.

Keywords:

Denoising Fixpoint control loss Hierarchical network Neural style transfer User control

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Neural style transfer re-renders content images using style image features.
Existing methods often lack user control over style intensity and detail preservation.
Previous approaches utilize perceptual and fixpoint content losses, limiting stylistic freedom.

Purpose of the Study:

To propose the Hierarchical Style Transfer Network (HSTN) for image stylization.
To enable user control over the degree of style applied via a denoising parameter.
To improve the preservation of content image details during stylization.

Main Methods:

Developed the Hierarchical Style Transfer Network (HSTN).
Incorporated a fixpoint control loss for detail preservation.
Integrated a denoising CNN network (DnCNN) and denoising loss for style control.
Utilized encoder-decoder, DnCNN, and loss network blocks.

Main Results:

HSTN demonstrated superior fusion of style and content preservation compared to existing methods.
Achieved 12% better results than the second-best performing method in user evaluations.
Obtained a favorable trade-off between content (37.64%) and style (60.27%) classification scores.

Conclusions:

HSTN offers users significant liberty in controlling stylization intensity.
The proposed method excels in preserving content details while achieving effective style transfer.
HSTN represents a novel approach for controllable and high-fidelity image stylization.