Advancements in artificial intelligence have revolutionized real-time
video processing, enabling enhanced visual quality for applications in
surveillance, medical imaging, and entertainment. Traditional video
enhancement methods often struggle with balancing computational
efficiency and high-quality output, leading to degraded performance in
real-time scenarios. The primary challenge lies in preserving details
while reducing noise, motion artifacts, and frame inconsistencies,
particularly in low-resolution and fast-motion videos. This study
introduces an AI-driven real-time video enhancement framework that
integrates super-resolution techniques with optical flow-based motion
estimation. The proposed method employs a deep learning-based
Super-Resolution Generative Adversarial Network (SRGAN) to
upscale video frames while maintaining texture fidelity. Additionally,
an enhanced optical flow algorithm refines motion estimation,
minimizing temporal inconsistencies and improving frame transitions.
The combination of these techniques enables effective noise reduction,
sharper details, and smooth motion handling, making the framework
suitable for real-time applications. Experimental evaluations
demonstrate that the proposed approach significantly improves peak
signal-to-noise ratio (PSNR) and structural similarity index (SSIM)
compared to existing methods. The system achieves real-time
performance with minimal computational overhead, making it suitable
for deployment in live broadcasting, telemedicine, and security
surveillance. The results highlight the efficiency of integrating AI-
based super-resolution with optical flow in achieving superior video
clarity and motion coherence in real-time environments.
Sonia Victor Soans1, T. Prabakaran2, S. Vamshi Krushna3
University of Technology and Applied Sciences, Sultanate of Oman1, Joginpally B.R. Engineering College, India2, Samskruti College of Engineering and Technology, India3
Real-Time Video Enhancement, Super-Resolution, Optical Flow, Ai- Driven Video Processing, Motion Estimation
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 3 , Pages: 3523 - 3528 )
Date of Publication :
February 2025
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43
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