Review of Various Neural Style Transfer Methods: A Comparative Study

NST or neural style transfer has revolutionized the field of image processing by allowing the amalgamation of artistic styles to photographs. First introduced by Gatys et al., NST relies on a slow and iterative optimization process. However, recent advances have introduced faster and more efficient approaches, such as adaptive instance normalization (AdaIN) and Johnson's method. Gatys's method, which laid the foundation for NST, uses a convolutional neural network (CNN) to extract information about the content of an image and artistic features or style of an image. Based on reducing the heterogeneity between features of content images and style images, this procedure although ultramodern, is tedious. By reconceptualized model normalization, AdaIN initiated an innovative technique for rapid amalgamation of content and style features from random images. It terminates the requirement of time-consuming optimization by focusing on real-time artistic shift with excellent mouldability. Johnson's technique involves a unique learning experience using sensory loss and previously learned interactions to demonstrate high-contrast tasks This allows for better preparation through the use of localized activities and in imagery on instantaneous analysis, resulting in a greater mixture of the two parts. This research studies the comprehensive insight into NST techniques and their evolution, highlighting potential approaches for image processing and the resolution method.