CNN-based image style transformation--Using VGG19

Abstract

eural Style Transfer is a widely used approach in the field of computer vision, which aims to generate visual effects by integrating the information contained in one image into another. In this paper, this work presents an implementation of neural style transfer using TensorFlow and the VGG19 model. The proposed method involves loading and preprocessing the content and style images, extracting features from both images using the VGG19 model, and computing Gram matrices to capture the style information. A StyleContentModel class is introduced to encapsulate the style and content extraction process. The optimization process is performed using the Adam optimizer, where gradients are applied to iteratively update the generated image. The number of epochs and steps per epoch can be adjusted to control the optimization process and achieve desired results. Experiments show that we are effective in generating an image that is able to integrate the content of one image into the other. The generated images exhibit visually appealing characteristics and showcase the potential of neural style transfer as a creative tool in image synthesis. Future work may involve exploring different variations of the style transfer algorithm, optimizing hyperparameters, and evaluating the performance on a wider range of image datasets. Additionally, the integration of other deep learning architectures and techniques could further enhance the capabilities of neural style transfer.