Artwork style transfer model using deep learning approach
Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.