Combining Style Transfer in a Frame with Random Rotation Using Convolutional Neural Networks

Style transfer is a research area that has garnered significant attention. This technology allows the style of one image to be transferred into the content of another image. Extensive research has been conducted in the field of style transfer, aiming to accelerate processing and produce beautiful and high-quality images. One application of this technology could be in generating beautiful designs for printing in industries such as tile and carpet manufacturing, where creating such intricate patterns by human hands would be challenging. In this article, a simple method is proposed for transferring multiple styles that have been randomly rotated, resulting in more unique designs compared to using a single style for transfer. This method can enhance the diversity and quality of the generated images. Evaluation of this method was conducted by surveying 25 individuals who compared our method to other existing methods. Ultimately, our method received the highest approval rating. The outcome of this research facilitates the creation of artworks more efficiently, offering a viable alternative to human-designed patterns.