Haze and fog removing from videos and images has got massive concentration in the field of video and image processing because videos and images are severely affected by fog in tracking and surveillance system, object detection. Different defogging techniques proposed so far are based on polarisation, colour-line model, anisotropic diffusion, dark channel prior (DCP) etc. However, these methods are unable to produce output image with desirable quality in the presence of dense fog and sky region. In this study, the authors have proposed a novel fog removal technique where DCP is applied on the low-frequency component of empirical wavelet transformation coefficients of the foggy input image. They apply unsharp masking on wavelet coefficients of the embedded wavelet transformed image for improving the sharpness of the output image. Later contrast limited adaptive histogram equalisation technique is used as a post-processing task to the inverse transformed image for producing the sharp and high contrast output. Finally, the colour and intensity of the contrast-enhanced image are uplifted through S-channel and V-channel gain adjustment. The proposed method provides significant improvement to the overall quality of the output image compared to contemporary techniques. The quantitative and qualitative measurements confirm the claims.

中文翻译：

---

基于经验小波变换的暗通道先验除雾

从视频和图像中去除雾霾已经在视频和图像处理领域集中，因为视频和图像在跟踪和监视系统，目标检测中受到雾的严重影响。迄今为止，提出的不同的除雾技术基于偏振，色线模型，各向异性扩散，暗通道先验（DCP）等。但是，在浓雾和天空区域的存在下，这些方法无法生成具有理想质量的输出图像。在这项研究中，作者提出了一种新颖的除雾技术，其中DCP应用于有雾输入图像的经验小波变换系数的低频分量。他们对嵌入的小波变换后的图像的小波系数应用了不清晰的遮罩，以提高输出图像的清晰度。后期对比度受限的自适应直方图均衡技术被用作逆变换图像的后处理任务，以产生清晰，高对比度的输出。最后，通过S通道和V通道增益调整可以提高对比度增强图像的颜色和强度。与现代技术相比，该方法大大改善了输出图像的整体质量。定量和定性的测量结果证实了权利要求。与现代技术相比，该方法大大改善了输出图像的整体质量。定量和定性的测量结果证实了权利要求。与现代技术相比，该方法大大改善了输出图像的整体质量。定量和定性的测量结果证实了权利要求。