摘要： 现有雾天图像处理方法能够实现较好的去雾效果，但会丢失部分细节并产生噪声放大的问题。将暗原色先验与基于TV、BH规则项的变分模型相结合，提出一种新的变分去雾模型H-TVBH。根据暗原色先验原理估计图像的初始透射率，采用四叉树分解估计大气光值，将初始透射率和大气光值输入H-TVBH模型中，采用分裂Bregman算法和快速傅立叶变换并引入辅助变量和Bregman迭代参数，通过交替迭代求得优化后的透射率和去雾图像。实验结果表明，H-TVBH在增强图像对比度的同时能够有效抑制图像中的噪声，保留图像的纹理细节，使去雾图像更加清晰自然。

关键词: 图像去雾, 暗原色先验, 变分模型, 分裂Bregman算法, 快速傅里叶变换

Abstract: The existing foggy image processing methods can achieve good dehazing effect, but some details are often lost, and noise amplification is easy to occur in the noisy areas.In order to solve these problems, a new variational dehazing model, H-TVBH, is proposed based on dark channel priori and the variational model that uses Total Variation(TV) and Bounded Hessian(BH) rule terms.The initial transmittance of the image is estimated according to the dark channel prior principle.At the same time, the atmospheric light value is estimated by quadtree decomposition.Then the obtained initial transmittance and atmospheric light value are applied to the proposed model.After that, the auxiliary variables and Bregman iteration parameters are introduced, and the split Bregman algorithm as well as fast Fourier transform is adopted to solve the optimized transmittance and dehazing image through alternate iterations.Experimental results show that the proposed algorithm can enhance the image contrast while effectively suppressing the noise in the image, retain the image texture details, and make the image clearer and more natural.

Key words: image dehazing, dark channel prior, variational model, split Bregman algorithm, fast Fourier transform

中图分类号: 

• TP391