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Deep Layer Aggregation Architectures for Photorealistic Universal Style Transfer.

Marius Dediu1, Costin-Emanuel Vasile1, Călin Bîră1

  • 1Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 060042 Bucharest, Romania.

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This study enhances deep learning for photorealistic style transfer by improving image reconstruction and stylization. The novel approach uses deeper feature aggregation within the PhotoNet architecture for superior results.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Photorealistic universal style transfer aims to apply the artistic style of one image to the content of another.
  • Existing methods like PhotoNet offer a strong baseline but can be improved for image quality and stylization fidelity.

Purpose of the Study:

  • To enhance the PhotoNet architecture for improved photorealistic universal style transfer.
  • To achieve superior image reconstruction and more aesthetically pleasing stylization compared to existing methods.

Main Methods:

  • Introduced a deep learning approach extending the PhotoNet network.
  • Incorporated additional feature-aggregation modules for deeper fusion of content and style information.
  • Proposed several deep layer aggregation architectures as wrappers over PhotoNet.

Main Results:

  • Achieved better image reconstruction quality.
  • Demonstrated more pleasant and enhanced stylization effects.
  • The proposed aggregation modules effectively fuse content and style information across decoding layers.

Conclusions:

  • The enhanced PhotoNet architecture with deep layer aggregation significantly improves photorealistic universal style transfer.
  • This approach offers a promising direction for high-quality image stylization in deep learning.