Dynamic Neural Style Transfer for Artistic Image Generation using VGG19
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
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.
