Denoising has long posed a significant challenge for security cameras, even with the denoising algorithms, on which modern security cameras heavily rely. These algorithms often lead to a degradation of image edges, especially in low illuminance conditions. To tackle this problem, a low illuminance image denoising algorithm is proposed, which aims to minimize the loss of edge information while reducing noise based on pixel value deviations. In light of the human visual system, we construct an image pyramid from the pixel values, allowing us to analyze local and global pixel value differences and mitigate the noisy influence on pixels of medium value. Experimental results show that the proposed algorithm removes the noise signal in nighttime road images, preserves edge details, enhances the overall image quality compared to the classic wavelet denoising algorithm, and improves the signal-to-noise ratio according to the Imatest SNR test.