Implementing Neural Style Transfer Using Python-Based Keras Library And VGG-19 Convolution Neural Network

Abstract

A series of software algorithms known as Neural Style Transfer (NST) makes use of a neural network to modify and alter media scenes and environments. Unlike conventional techniques, NST is used in image and video editing software to enable image stylization based on a general model. Because of this, NST has become a hot topic in the entertainment sector. Experienced editors and media producers can produce content more quickly and make it available for public consumption. This work presents a comprehensive analysis of the state-of-the-art developments in Neural Style Transfer, including all relevant features including still photos and videos. The writers examined the various architectures in use and contrasted their benefits and drawbacks. This research makes use of the CNN (Convolution Neural Network) VGG-19 model and the Python-based Keras framework. It aims to give Deep Learning models the ability to distinguish between style representations and content images. The position of the style and content photos creates the illusion that the content image has been "Painted" to resemble the style reference image. It is an application for computer vision that uses image processing techniques along with deep convolution neural networks. The mean squared error between the content's features and the output image is the content loss. Using the features taken from the content and style images, the VGG-19 model feature extractor is utilized to determine the content loss and style loss. The difference in mean squared is the style loss