针对雾霾天气图像中雾霾浓度分布不均以及色彩失衡等问题, 提出一种将暗通道先验算法与带色彩恢复因子的多尺度Retinex算法相结合的分块调节图像增强算法.对大气散射模型以Retinex图像模型中的尺度进行线性映射, 得到一个同时具有大气散射模型中的透射率和大气光照值以及Retinex图像模型中入射图像的多参数新模型.根据模型获取去除大气散射图的新原图, 并利用不同尺度的引导滤波计算获得整幅图像的入射图像, 再结合大气散射光图以及色彩恢复因子得到最终的高频细节图.针对图像中雾霾浓度分布不均的情况将整图划分为多个区域小块, 用融合后的算法计算每个区域小块的动态截断值, 根据不同的动态截断值可以对整幅图像的高频细节进行动态调整, 从而得到多幅局部最优图像, 将得到的图像进行像素级等权融合, 最后可得到保证各局部细节的最优图像.将本文算法与现有算法在主观视觉和客观评价两方面进行了实验对比, 结果表明, 该方法可以有效解决图像雾气不均匀以及色彩失衡等问题, 明显提高了去雾后图片的质量.

Aiming at the uneven distribution of haze concentration and color imbalance in haze weather images, a block-based image enhancement algorithm is proposed, which combines dark channel prior algorithm with multi-scale retinex with color restore algorithm. The idea of the algorithm is to map the atmospheric scattering model linearly to the scale of the Retinex image model, and get a new multi-parameter model with both transmittance and illumination value in the atmospheric scattering model and the incident image in the Retinex image model. The new original image of removing atmospheric scattering image is obtained according to the model, and the incident image of the whole image is calculated by using different scales of guided filtering. Then, the final high-frequency detail image is obtained by combining the atmospheric scattering image and color restoration factor. The whole image is divided into several regions in view of the uneven distribution of haze concentration in the image and the dynamic truncation value of each regional block is calculated by using fusion algorithm. According to different dynamic truncation values, the high frequency details of the whole image can be dynamically adjusted to obtain multiple local optimal images. These images are equal-weighted at the pixel level, and finally the optimal image can be obtained which can retain the local details. The proposed method is experimentally compared with other existing methods on subjective visual effect and objective evaluation, the results show that the method can effectively solve the problems of non-uniform fog and color imbalance in images, and obviously improve the quality of the image after fog removal.