基于能量泛函的快速去雾算法

为了快速复原雾霾退化图像场景辐照图,提出一种基于能量泛函的模型求解算法。利用大气退化模型,首先估计降质图像的大气光;针对图像是否包含天空区域分开进行求解,较传统固定模式的求解算法更为准确有效;通过白平衡运算简化求解模型,建立新的环境光项表达式;尔后利用暗通道先验估计暗通道图像;根据假设和先验信息,构建暗通道图像与环境光项的能量泛函模型,引入L1和L2范数变换模型,通过切分Bregman迭代算法求解图像的环境光;最后将环境光项代入简化模型中反解出复原图像。通过实验验证,算法对于雾霾退化图像恢复效果较好,且较传统复原算法具有更高的运算效率。

A Fast dehazing algorithm based on energy functional

A new image dehazing algorithm based on energy functional is proposed for the purpose of recovering scene radiance of haze degraded images. Considering the atmospheric degradation model, skylight is firstly estimated using two different methods whether the degraded images include the sky area or not, having better results than traditional ones. Then the white balance is conducted in order to simplify the scattering model and build new form of the airlight. And dark channel prior is performed to evaluate the dark channel map. The prior knowledge and the prior assumptions are used to construct the energy functional model between the dark channel map and the airlight, bringing norm and norm into the model for better computation. Meanwhile, the split Bregman method is employed to obtain the airlight of the image. Finally, the scene radiance is recovered by the model. Experiment results show that the algorithm is valid and robust for haze images and enhance the visibility with high efficiency.