Joint Enhancement of Historical News Video Quality Using Modified Conditional GANs: A Dual-Stream Approach for Video and Audio Restoration

Xuzhong Jia, Hanqing Zhang, Chenyu Hu, Guancong Jia

Abstract


The preservation and enhancement of historical news video archives represent a critical challenge in digital archiving. This paper proposes a novel dual-stream approach leveraging modified conditional Generative Adversarial Networks (GANs) for simultaneous enhancement of video and audio quality in historical news footage. The framework incorporates parallel processing pathways for video and audio restoration, connected through an innovative feature fusion mechanism. The architecture introduces several key improvements, including temporal-aware processing modules, multi-scale discriminators, and adaptive feature fusion strategies. Comprehensive experiments conducted on a diverse dataset of historical news broadcasts from 1960-2000 demonstrate significant improvements over existing methods, achieving a 35.2% increase in Peak Signal-to-Noise Ratio (PSNR) and 29.8 dB improvement in audio Signal-to-Noise Ratio (SNR). The proposed framework maintains temporal coherence while preserving content authenticity, addressing critical challenges in archival media restoration. Quantitative evaluations show superior performance across multiple quality metrics, while qualitative assessments confirm enhanced perceptual quality and historical accuracy preservation. The experimental results validate the effectiveness of the dual-stream approach in historical news video restoration, establishing a new benchmark for automated archival media enhancement.


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