基于图像退化模型的图像去雾算法

在雾、霾之类的恶劣天气下拍摄的图像，由于存在大气的散射作用，使得物体特征难以辨认，严重影响了图像的视觉效果，同时还妨碍了图像的特征提取. 因此，需要利用去雾技术对图像进行增强和修复，以改善视觉效果和方便后期处理. 本文针对暗原色先验去雾算法耗时长和处理效果不佳等问题，提出了一种改进的自适应边界约束去雾算法. 同时，引入了信息熵和平均梯度对其进行客观评价，对比实验结果表明该方法运算速度快，在细节处理上效果更好.

Image Defog Algorithm Based on Image Degradation Model

For the images captured in the bad weather like fog or haze, the atmospheric scattering effect not only seriously affects the visual appearance of the image, but also hinders the image feature extraction. Therefore, it needs de-fog technology for image enhancement and restoration, to improve the visual effects and convenience of post-processing. Because that dark colors prior algorithm is time consuming and has poor treatment effect etc., we put forward an improved algorithm of boundary constraints defogging algorithms. At the same time, we introduce information entropy and average gradient to evaluate the algorithm objectively. Comparison of experimental results shows that this method has a high computing speed, and better effects on the deal.