Neural Style Transfer (NST) is a deep learning technique that applies the artistic style of one image onto another while preserving the content structure. This is achieved using pretrained CNNs such as VGG-19.
Feature Extraction: The content image and style image are processed through multiple CNN layers to extract content and style features.
Optimization Process: A loss function minimizes the difference between the generated image’s content and the original image while maximizing its stylistic resemblance to the reference style image.
Gradient Descent: The network iteratively updates pixel values to merge style and content into a single image.
NST is widely used in digital art, augmented reality, and creative AI applications to produce visually unique outputs.