This study presents a dehazing algorithm specifically tailored for improving the quality of surveillance images impaired by foggy weather. The core research areas encompass atmospheric light estimation and the dehazing process. First, a perfect white balance method is employed for preprocessing surveillance video frames to remove the color influence caused by fog in the images. Second, based on an in-depth analysis of surveillance scene characteristics, a depth-based atmospheric light estimation method is proposed to accurately obtain crucial atmospheric light information within fog. Subsequently, a cross Bilateral filter is introduced, carefully balancing filtering efficacy and edge preservation to effectively eliminate noise in foggy images. Finally, by subtracting the estimated atmospheric light component from surveillance video frames, clear dehazed surveillance images are obtained. The performance evaluation of the proposed algorithm on six datasets yields the following metrics: the highest Peak Signal-to-Noise Ratio (PSNR) is 8.507, the Structural Similarity Index (SSIM) is 0.826, the Universal Image Quality Index (UQI) is 0.732, and the information entropy is 7.663. These quantified performance indicators clearly illustrate the remarkable performance of the proposed algorithm in dehazing tasks, providing solid support for its practical application in the field of surveillance image enhancement.