## 4 Experimental Results
### Performance Evaluation  
The accuracy of detection under different conditions largely depended on the quality of image defogging, deraining, and desnowing and image enhancement. In this study, we compared our image processing module and image enhancement module (TRA + DLIE) with existing dehazing methods (AOD-NET, GridDehazeNet), deraining methods (EfficientDeRain, ADMM-ResNet), and desnowing methods (U-DenseNet, ALL In One). To ensure a fair comparison, we retrained and evaluated these methods on the same training and testing datasets (VOC [63], HAZE [64], FTOD). We used the MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), and SSIM (structural similarity) metrics to measure image quality and similarity. Improvements in PSNR and SSIM metrics implied that the processed images were closer to the original images, exhibiting higher quality and better preservation of image details and structures. \[PSNR=20\cdot\log_{10}\left(\frac{\text{MAX}_{\text{I}}}{\text{MSE}}\right) \tag{32}\]  
In the given expression, \(\text{MAX}_{\text{I}}\) represents the maximum achievable pixel value within the image. SSIM is another metric for comprehensive image quality assessment, evaluating image similarity. To bolster the consideration of structural similarity in the image, we introduced a correction term, denoted as \(\varphi\). The enhanced SSIM formula is expressed as follows:  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2}+\varphi)}{(\mu_{x}^ {2}+\mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}+\varphi)} \tag{33}\]  
where \(\mu\), \(\mu^{2}\), and \(\sigma\) respectively represent the mean, variance, and covariance of the images. The addition of a correction term \(\varphi\) for the structural similarity calculation made the evaluation metric more elastic, adapting to different types and qualities of images, and enhancing the algorithm's performance under various adverse weather conditions. The objective evaluation results of weather-removed images of different datasets are shown in Table 2. For comparison, the proposed (TRA + DLIE) method outperformed the aforementioned algorithms for objective evaluation metrics efficiency in dehazing, deraining, and desnowing. Figure 4 illustrates the results using the aforementioned desnowing methods. While other deep learning-based techniques exhibit varying levels of image artifacts and incomplete dehazing, our proposed approach to dehazing, deraining, and desnowing excelled at removing haze, raindrop textures, and snowflakes while simultaneously enhancing contrast and saturation. This comprehensive enhancement significantly improved image quality, laying a solid foundation for subsequent object detection tasks. Figure 5 visually demonstrates how our small CNN network predicted the image enhancement parameters (WB, gamma, contrast, tone) for the DLIE module in three distinct examples. Leveraging specific image information such as brightness, color, hue, and weather features, the small CNN network learned tailored parameter sets for each image. By filtering out weather-related factors, the images underwent processing through the DLIE module, resulting in enhanced visual effects, improved image clarity, and enriched details, ultimately facilitating more accurate detection.