Zero-Shot Illumination and Noise Estimation for Edge-Deployable Low-Light Image Enhancement in IoT Systems

Abstract

In Internet of Things (IoT) systems, such as autonomous vehicles and robots, visual perception under low-light conditions is often severely degraded due to insufficient illumination and intensive sensor noise. While numerous methods have been developed for low-light enhancement, their reliance on paired annotation and computationally heavy architectures limits their applicability for downstream perception tasks, especially on resource-constrained platforms. To address these issues, this article proposes REZS-Edge, a novel zero-shot enhancement framework that eliminates the need for paired supervision and is specifically designed for efficient edge execution. First, by integrating Retinex theory with the noise-to-noise paradigm, the method jointly estimates illumination conditions and suppresses noise through a unified pipeline. Then, a zero-shot training strategy with global and local illumination constraints facilitates fully unsupervised optimization and enhances generalization. Furthermore, the framework incorporates structural re-parameterization to enable model acceleration at the inference phase while maintaining performance. Extensive experiments demonstrate that REZS-Edge achieves state-of-the-art results on low-light image enhancement (LLIE) datasets, including LOLv1 and LOLv2-Real, with a notably fast inference speed of 6.25 ms on a commercial SoC platform Snapdragon 8 Gen 3. More importantly, the method exhibits strong generalization on critical downstream tasks such as low-light object detection, instance segmentation, and pose estimation. These advantages make REZS-Edge particularly valuable for autonomous driving and robotic systems that require reliable, efficient, and all-weather visual perception, offering a practical and deployable solution for real-time enhancement under challenging lighting conditions.