基于区间估计与透射率自适应约束的去雾算法

针对去雾算法透射率估计不足与结果偏色等问题，提出了一种基于最小通道区间估计与透射率自适应约束模型的图像去雾算法。首先，采用不同尺寸最大值操作得到有雾图像的亮通道，并结合均值处理和频域滤波得到大气光估计；其次，从大气成像理论出发，以有雾图像最小通道为约束，分别以平面模型和自适应映射模型拟合无雾图像最小通道上下边界，并获得无雾图像最小通道和透射率初始估计；最后，对透射率作滤波平滑与自适应边界约束，得到优化透射率，并根据大气散射模型得到复原结果。实验表明:所提算法复原结果颜色自然、亮度适宜、去雾彻底、细节信息丰富且时间复杂度较低，有效解决了透射率估计不足和偏色等问题。 

Dehazing algorithm based on interval estimation and adaptive constraints of transmittance

In order to solve the problems such as insufficient transmittance estimation and color cast of results of dehazing algorithms, an image restoration algorithm based on minimum channel interval estimation and transmittance adaptive constraint model is proposed. Firstly, bright channel of hazy image is obtained by using maximum operation of different sizes, and average value processing and frequency domain filtering are combined to get the atmospheric light estimation. Secondly, starting from the atmospheric imaging theory, minimum channel of hazy image is regarded as a constraint, then upper and lower boundaries of minimum channel of hazy image are fitted by plane model and adaptive mapping model respectively, and minimum channel of dehazed image and initial transmittance estimation are obtained. Finally, the initial transmittance can be refined by filter smoothing and adaptive boundary constraints to obtain the optimized transmittance, and according to atmospheric scattering model, restoration results are obtained. Experiments show that the restoration results of the proposed algorithm have natural colors, appropriate brightness, thorough degree of dehazing, rich detailed information and low time complexity, which effectively solves the problems of insufficient transmittance estimation and color cast.