Controllable Low Light Restoration of Traffic Camera Footage across Nighttime Urban Traffic Sequences
DOI:
https://doi.org/10.71465/jgs54Keywords:
Low-light enhancement, Traffic surveillance, Controllable restoration, Spatiotemporal modelingAbstract
The proliferation of smart city infrastructure has significantly increased the reliance on continuous traffic monitoring systems. However, capturing high-quality video sequences in low-light conditions remains a formidable challenge due to severe sensor noise, motion blur, and color distortion. Traditional image and video restoration techniques often apply a rigid, one-size-fits-all approach, which fails to accommodate the highly variable illumination and weather conditions intrinsic to urban traffic environments. This paper presents a comprehensive investigation into controllable image restoration methodologies specifically tailored for low-light traffic camera footage sequences. By leveraging advanced spatiotemporal modeling and user-defined conditioning variables, we propose a theoretical framework that allows human operators or automated downstream perception systems to dynamically adjust the restoration parameters, such as exposure levels, noise suppression strength, and color saturation, in real-time. The study meticulously details the architectural design required for extracting robust temporal features across sequential frames while maintaining temporal consistency and mitigating flickering artifacts. Through extensive analysis of current deep learning paradigms and theoretical evaluations of structural designs, we elucidate the mechanisms by which control vectors can seamlessly modulate feature representations. The findings underscore the critical importance of balancing computational efficiency with perceptual quality in edge-deployed surveillance systems, offering a significant contribution to the broader field of intelligent transportation and computer vision.
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