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Utilizing machine vision to acquire road environment information is a crucial factor influencing the performance of advanced driver assistance systems, but under low-light conditions, obtaining high-quality road information directly through vision becomes challenging, necessitating image enhancement. Due to uneven illumination in low-light conditions, existing enhancement methods often lead to issues such as glare and blurred details in bright areas. In this paper, we propose an optimized Multi-Scale Retinex (MSR) image enhancement algorithm. Firstly, RGB images are converted to the YUV format, and the MSR algorithm is applied to enhance the Y channel. The local background brightness is then incorporated into the Just Noticeable Difference (JND) model to establish a non-linear relationship model between background brightness and adjustment factors. This enables adaptive adjustment of image enhancement intensity. Subsequently, a non-linear bilateral filtering function is applied to smooth the adjusted image, followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance its contrast. Finally, the resulting image is fused with the original image in a 1:1 ratio. Experimental results indicate that, compared to the combination of MSR and histogram equalization, the proposed method achieves a 4.5% increase in standard deviation, a 7% increase in information entropy, and a 25% improvement in peak signal-to-noise ratio.
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