Dynamic Neural Style Transfer for Artistic Image Generation using VGG19
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913653266055168 |
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| author | Kashyap, Kapil Garg, Mehak Fargose, Sean Nair, Sindhu |
| author_facet | Kashyap, Kapil Garg, Mehak Fargose, Sean Nair, Sindhu |
| contents | Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of images, applying texture synthesis techniques. Nevertheless, a number of current techniques continue to encounter obstacles, including lengthy processing times, restricted choices of style images, and the inability to modify the weight ratio of styles. We proposed a neural style transfer system that can add various artistic styles to a desired image to address these constraints allowing flexible adjustments to style weight ratios and reducing processing time. The system uses the VGG19 model for feature extraction, ensuring high-quality, flexible stylization without compromising content integrity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09420 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Neural Style Transfer for Artistic Image Generation using VGG19 Kashyap, Kapil Garg, Mehak Fargose, Sean Nair, Sindhu Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of images, applying texture synthesis techniques. Nevertheless, a number of current techniques continue to encounter obstacles, including lengthy processing times, restricted choices of style images, and the inability to modify the weight ratio of styles. We proposed a neural style transfer system that can add various artistic styles to a desired image to address these constraints allowing flexible adjustments to style weight ratios and reducing processing time. The system uses the VGG19 model for feature extraction, ensuring high-quality, flexible stylization without compromising content integrity. |
| title | Dynamic Neural Style Transfer for Artistic Image Generation using VGG19 |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.09420 |