VEGAN: CCTV video quality enhancement with GAN-based foreground separation and super-resolution

As countries rapidly transition toward smart cities, closed-circuit television (CCTV) surveillance systems are playing an increasingly vital role in ensuring public safety and enabling urban analytics. However, the visual quality of CCTV footage is often degraded by environmental factors (e.g., motion blur, low resolution, and poor illumination), which significantly impact the quality of service, as well as the reliability and effectiveness of these systems. To address these issues, this research proposes a generative adversarial network (GAN)-based framework, named VEGAN (Video Enhancement with Generative Adversarial Network), that combines reconstruction, adversarial, and facial component losses with adaptive balancing to optimise visual sharpness, temporal stability, and identity preservation. VEGAN integrates four key components: (1) a pixel counter, which identifies high and low quality frames; (2) Modified MoCoGAN, which separates foreground and background features to disentangle content from motion; (3) a Recurrent Neural Network, which captures complex temporal and motion patterns; and (4) a Super-Resolution module, which enhances low-quality pixels to recover fine spatial details. The enhanced foreground generated by these combined modules is seamlessly fused with a high-quality background frame, resulting in substantially improved overall video quality. Experimental evaluations demonstrate VEGAN’s effectiveness, achieving an average Learned Perceptual Image Patch Similarity (LPIPS) score of 0.041 and a Video Multimethod Assessment Fusion (VMAF) score of 56.13, indicating significant perceptual and quantitative improvements. These findings highlight VEGAN’s effectiveness in video-based analytics, supporting more accurate performance in tasks such as event detection and activity recognition.