针对最小滤波导致的光晕、伪影及透射率估计不足引起的天空失真等问题,提出了一种基于二次约束与雾气分布的去雾算法。首先,利用Canny检测算子得到有雾图像的粗纹理分布,在场景深度的基础上得到有雾图像的雾气分布图。其次,根据图像特征,提出了一种基于自适应雾气系数的二次约束模型,估计出清晰图像的最小通道图,并利用大气散射模型得到粗略透射率。然后通过自适应下边界约束与双边滤波进行透射率优化,并通过形态学操作改进局部大气光。最后根据复原模型得到去雾图像。实验表明,所提算法在主、客观评价中均取得了理想的效果,较好解决了去雾图像中的光晕、伪影与天空失真等问题。

Aiming at the problems of halo and artifacts caused by minimum filtering as well as sky distortion caused by insufficient transmittance estimation, one dehazing algorithm is proposed, which is based on quadratic constraints and haze distributions. Firstly, the Canny detection operator is used to obtain the coarse texture distributions of hazy images, and the mist distribution maps of hazy images are obtained on the basis of scene depth. Second, according to the image characteristics, a quadratic constrain model based on adaptive haze coefficients is proposed to estimate the minimum channel of clear images, and the atmospheric scattering model is used to obtain rough transmittance. Third, the transmittance is optimized through adaptive lower boundary constraints and bilateral filtering, and local atmospheric light is improved through morphological operations. Finally, the dehazing images are obtained according to the restoration model. Experiments show that the proposed algorithm has achieved ideal results in both subjective and objective evaluations, and it can better solve the problems including halos, artifacts, and sky distortion in dehazing images.