In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.

Atmospheric scattering caused by fog degrades image quality and significantly reduces the reliability of computer vision systems. Existing dehazing studies have mainly evaluated dehazing performance using pixel-level metrics such as PSNR and SSIM. However, these metrics do not fully reflect the actual impact of dehazing on downstream object detection performance. Therefore, this paper treats image dehazing as a preprocessing step for object detection in foggy environments and analyzes its effect using standard object detection evaluation metrics. The experimental results demonstrate that, under three fog-density conditions, $\beta =0.005$, $0.010$, and $0.020$, images processed by the DL-U-Net-based dehazing method achieved higher $\mathrm{mAP}@0.5$ values than the corresponding original hazy images, with relative improvements of $+0.39\%$, $+6.60\%$, and $+13.37\%$, respectively. Furthermore, under the dense fog condition of $\beta =0.020$, Recall improved more substantially than Precision. These results indicate that, as fog density increases, dehazing preprocessing becomes more effective in restoring object structural information, reducing missed detections, and enhancing downstream object detection performance.