Neural Style Transfer for Artistic Image Generation Using CNN and Deep Learning

Neural Style Transfer (NST) is a deep learning-based technique that generates artistic images by blending the content of one image with the style of another. This project implements NST using a pre-trained VGG-19 convolutional neural network, where the network extracts hierarchical feature representations of images. A combined loss function, consisting of content loss and style loss, is optimized through iterative updates to produce a stylized image that maintains the semantic structure of the content while reflecting the artistic characteristics of the chosen style. The proposed system demonstrates applications in digital art, photo editing, film production, and personalized creativity tools. Furthermore, the project highlights the potential of extending this technique to real-time video style transfer, mobile applications, and multistyle generation, making neural style transfer a powerful tool in the intersection of art and artificial intelligence. This project not only bridges the gap between technology and art but also empowers non-artists to create visually appealing designs with ease. The experimental results confirm that NST can successfully generalize across diverse image domains and artistic styles.