Change Detection in Wide-Field Video Images Based on Low Illumination Enhancement

In low illumination environments, the image quality of eagle-eye surveillance devices is significantly degraded by high-density random noise and inhomogeneous illumination. In addition, the change targets in the surveillance area are usually small, which further increases the difficulty of change detection CD , and is prone to false positives and negatives. In this paper, we propose a new unsupervised CD method for small targets. Specifically, under low illumination, the image is pre-enhanced using the bright channel prior and Single-Scale Retinex SSR algorithm to improve image quality two difference images DIs are generated by the arctangent operator and the Chi-Square Transform CST , and the difference information is fused using the improved multiplicative fusion MTF technique to ensure the completeness of the details in the change region and suppress the noise. Particularly, for areas with few changes or no changes, we propose a threshold segmentation method based on Log-Normal Distribution Histogram Fitting Error Minimization LNDFEM to achieve the segmentation of change regions. Experimental results demonstrate that the proposed method outperforms comparison algorithms in terms of overall error, F1-Score, and Kappa coefficient, and exhibits stronger robustness.