Abstract
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.
Highlights
Nowadays, tremendous efforts have been put into accelerating different techniques to assist computers in performing human-like tasks such as classification and communication due to the fast-growing artificial intelligence advancements
The standard model was able to apply the style of the given image to the content image
The dataset used for implementation includes the Flirck8k dataset (8100 images, 1Gb) and the Microsoft Coco dataset (80000 images, 13Gb)
Summary
Tremendous efforts have been put into accelerating different techniques to assist computers in performing human-like tasks such as classification and communication due to the fast-growing artificial intelligence advancements. Preserving artistic features thereby becomes noteworthy for both human duties and technology, as they represent the heritage and culture through time [Doulamis and Varvarigou, 2012]. The first image transforming algorithm was founded as an innovation to change a picture’s style while preserving its shape, which has drawn substantial attention [Gatys et al, 2016b]. The term ‘neural style transfer’ was introduced to indicate a machine learning technique for converting an image’s style from an initial to another by blending a content image into a style reference image, such as artworks from reputed artists.
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