Edge computing-based Generative Adversarial Network for photo design style transfer using conditional entropy distance

Abstract

In recent years, cell phone photography systems have made significant advancements due to their widespread adoption. However, processing the large amount of data generated by these systems on the cloud often leads to high network latency and bandwidth requirements, limiting computational efficiency. To address this challenge, this study introduces a novel design style transfer method for generating high-quality stylized photos in cell phone photography systems using Generative Adversarial Networks (GANs) with an edge computing-based approach. The proposed method aims to enhance the visual appeal of cell phone images by mimicking the artistic style of paintings through abstraction and emphasis on key features. To achieve this objective, a conditional GAN is trained on meticulously labeled data pairs that identify crucial regions in images. This approach offers an efficient solution for computationally intensive tasks by utilizing inference on edge computing nodes. This effectively reduces network latency and bandwidth requirements while improving computational efficiency. Additionally, a penalty is introduced using conditional entropy distance to ensure consistent entropy levels across different photo samples, resulting in higher quality generated photos. Experimental results demonstrate that the proposed CEDGAN model achieves a detection accuracy ranging from 95% to 98%, with consistent results, thus laying a solid foundation for effective photo enhancement. When collecting important portions of materials, CEDGAN maintains a detection accuracy of around 90% to 95% without any significant difference in overall performance. As a result, this method opens up new possibilities for cell phone photography enthusiasts who desire to effortlessly create artistic and stylized photos using edge computing-based GANs. It offers a practical solution for combining technological innovation with creativity in photography.