中文摘要：

针对已有单幅图像去雾方法中存在的天空灰暗和对比度增强不足等问题,提出基于大气光自适应校正与透射率鲁棒性优化的高可见度图像去雾算法.该算法采用白平衡和伽马校正对输入图像进行预处理,以提升亮度、增强对比度并避免出现严重的偏色现象.为了防止大气光值估计过高,提出一种基于天空检测的大气光自适应校正方法,以获得更明亮的天空区域复原效果.最后通过检测光晕像素和透射率上下文一致性推断来识别透射率不可靠的像素,并在可靠透射率保持项、不可靠透射率插值项以及相似像素透射率关联项的约束下设计了透射率鲁棒性优化模型,以对不可靠透射率进行校正.实验结果表明,文中算法获得的透射率更符合场景中的深度变化趋势,使得去雾结果具有较高的清晰度、对比度与色彩饱和度,且天空区域也显得更为自然.

英文摘要：

To address the dusky sky and the insufficient contrast enhancement of the existed single image dehazing methods,we propose a high visibility image dehazing algorithm with adaptive atmospheric light correction and robust transmission optimization strategies.The input image is preprocessed with white balance and gamma correction in this paper to increase brightness,enhance contrast and avoid the color cast problem.In order to prevent the atmospheric light from over-estimation,an adaptive atmospheric light correction strategy based on sky detection is put forward,which is beneficial to brighter restoration of the sky regions.Finally,we recognize those pixels with unreliable transmission by the detection of halo effect and the inference of transmission context consistency.And,we design a robust transmission optimization model to correct the unreliable transmissions,under the constraint of the reliable transmission maintenance term,the unreliable transmission interpolation term and the transmission correlation term among the similar pixels.Experimental results show that our transmission consists with the depth variation better,and the haze-free images possess high definition,contrast and color saturation,as well as natural restoration of sky regions.