ICTACT Journal on Image and Video Processing

K. Ramesh ¹, B. Muni Lavanya ², B. Rajesh Kumar ³, Narayan Krishan Vyas ⁴, Mohammed Saleh Al Ansari ⁵

Affiliations
1 Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, IN
2 Department of Computer Science and Engineering, JNTUA College of Engineering, IN
3 Department of Computer Science and Engineering, Dhanalakshmi Srinivasan College of Engineering, IN
4 Department of Electronics and Communication Engineering, Government Engineering College, Jhalawar, IN
5 Department of Chemical Engineering, College of Engineering, University of Bahrain, BH

Abstract

In computer vision and artistic expression, the synthesis of visually compelling images and the transfer of artistic styles onto videos have gained significant attention. This research addresses the challenges in achieving realistic image synthesis and style transfer in the dynamic context of videos. Existing methods often struggle to maintain temporal coherence and fail to capture intricate details, prompting the need for innovative approaches. The conventional methods for image synthesis and style transfer in videos encounter difficulties in preserving the natural flow of motion and consistency across frames. This research aims to bridge this gap by leveraging the power of Generative Adversarial Networks (GANs) to enhance the quality and temporal coherence of synthesized images in video sequences. While GANs have demonstrated success in image generation, their application to video synthesis and style transfer remains an underexplored domain. The research seeks to address this gap by proposing a novel methodology that optimizes GANs for video-challenges, aiming for realistic, high-quality, and temporally consistent results. Our approach involves the development of a specialized GAN architecture tailored for video synthesis, incorporating temporal-aware modules to ensure smooth transitions between frames. Additionally, a style transfer mechanism is integrated, enabling the transfer of artistic styles onto videos seamlessly. The model is trained on diverse datasets to enhance its generalization capabilities. Experimental results showcase the efficacy of the proposed methodology in generating lifelike images and seamlessly transferring styles across video frames. Comparative analyses demonstrate the superiority of our approach over existing methods, highlighting its ability to address the temporal challenges inherent in video synthesis and style transfer.

Keywords

Generative Adversarial Networks, Image Synthesis, Style Transfer, Video Processing, Temporal Coherence

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