摘要
基于图像复原的去雾算法中参数的估计容易造成去雾图像场景信息的丢失,对此,提出一种图像去雾新算法。在暗通道先验的基础上,通过对大气散射模型的分析,总结出雾气分布对暗通道图像的影响,并依此对外景图像进行加雾操作,利用加雾后的参考图像与外景图像中各点的景深关系完成透射率的估计,进而达到去雾目的。算法利用物理模型和多幅图像实现参数的估计,能够更好地保留场景信息。实验结果表明,该算法不仅去雾效果优于对比算法,在处理速度上也有明显改善。
Concerning that the parameter estimation in defogging algorithms based on image restoration is easy to cause the loss of scene information, a new defogging algorithm for single image was proposed. On the basis of the dark channel prior method, the atmospheric scattering model was analyzed and then the influence to dark channel image caused by fog distribution was summarized, which is the basis for adding fog to the outdoor images. The transmittance was estimated through the field depth relationship between the fog added reference image and the outdoor image to defogging. The algorithm used physical model and multiple images to complete the estimation of relevant parameters and had a better result in retaining scene information. The experimental results show that the proposed algorithm is more effective than the comparison algorithms, and its processing speed is also improved significantly.
出处
《计算机应用》
CSCD
北大核心
2015年第8期2291-2294,2300,共5页
journal of Computer Applications
基金
甘肃省科技厅自然科学基金资助项目(1310RJZA050)
甘肃省财政厅基本科研业务费资助项目(214138)
关键词
图像去雾
物理模型
图像加雾
透射率
图像复原
image defogging
physicals model
image plus fog
transmittance
image restoration
