Neural Art Style Transfer

The work advances the field of computational artistry by presenting a novel deep learning approach to neural style transfer—a technique first introduced by Gatys et al. [1]—which allows us to reimagine images in different artistic styles. Our approach employs the VGG19 network as a feature extractor, separating images into content and style representations. Content features are extracted from deeper network layers, while style is captured through Gram matrices [9] across multiple convolutional layers. The model refines the output image by reducing a combined loss function that harmonizes content retention with artistic style adaptation. Experimental results demonstrate successful synthesis of images that maintain structural integrity while adopting target artistic characteristics, advancing the application of deep learning in computational art generation. Furthermore, the study evaluates the effect of varying content–style loss weights, reports objective quality metrics such as PSNR and SSIM, and includes a TV-loss ablation to analyze the impact of regularization on stylization quality.