基于亮通道和暗通道结合的图像去雾

Image Defogging Based on Combination of Image Bright and Dark Channels

低照度环境下图片质量会下降。同时,悬浮在空气中的烟雾、粉尘等物质形成的雾、霾,会导致图像的细节模糊不清,对户外拍照和计算机视觉应用造成了极大的影响。因此,对退化图像进行去雾处理,提高图像质量,在图像处理和计算机视觉领域具有非常重要的应用价值。提出一种基于亮通道和暗通道结合的雾霾天气图像去雾算法。基于退化图像的物理模型,提出一种空气光散射模型,通过亮通道和暗通道的结合来估计大气光值和透射率。该算法可以解决有雾图像恢复时天空区域的颜色失真问题,恢复图像的细节和颜色,提高图像的视觉效果。仿真结果表明,本文算法优于多尺度Retinex图像去雾算法。

The picture quality is declined in the low illumination environment. Meanwhile, the fog and haze formed by smoke, dust and other substances suspended in the air will cause blurred image details, which have a great impact on outdoor photography and computer vision. Therefore, it has important application value for image processing and computer vision by defogging degraded images to improve image quality. We propose an image defogging algorithm based on the combination of bright and dark channel in fog and haze weather. A model of the air light scattering is proposed based on the physical model of degraded image. Air light value and transmissivity are estimated by using the combination of light channel prior and dark channel prior. The algorithm can solve color distortion problem of sky area when fog-free image is restored, recover the image details and color, and improve the vision effect of the image. Evaluation parameters are used to compare the image quality. Simulation results show that the algorithm proposed in this paper is better than multi-scale Retinex image defogging algorithm.