摘要
针对暗通道先验算法在景深较大处会出现颜色失真,且易受噪声干扰和运行时间久等问题,提出了一种基于多尺度小波变换的改进融合暗通道去雾方法。首先对有雾图像作二级小波分解,再对得到的高频分量利用软阈值去噪,对低频分量利用改进的自适应融合暗通道进行去雾。最后利用一个局部线性模型将高低频分量系数关联进行小波重构。实验结果表明,提出的算法具有较高的去雾效率,且能很好地提高去雾图像的质量。
Aiming at the problems of color distortion,noise interference and long running time in the dark channel prior algorithm with large depth of field,an image defogging method based on multi-scale wavelet transform and improved fusion dark channel was proposed.Firstly,the foggy image was decomposed by two-level wavelet transform,the high-frequency component was denoised by soft threshold,and the low-frequency component was defogged by improved adaptive fusion dark channel.Finally,a local linear model was used to correlate the high-frequency and low-frequency component coefficients for wavelet reconstruction.The experiment show that the proposed algorithm has high defogging effect and can improve the quality of defogging image.
作者
王文科
胡红萍
曹胜芳
WANG Wen-ke;HU Hong-ping;CAO Sheng-fang(School of Mathematics,North University of China,Taiyuan 030051,China)
出处
《科学技术与工程》
北大核心
2023年第15期6528-6535,共8页
Science Technology and Engineering
基金
山西省基础研究计划(20210302123019)
山西省回国留学人员科研项目(2020-104,2021-108)。
关键词
暗通道先验
小波变换
形态学梯度
图像去雾
透射率
dark channel prior
wavelet transform
morphological gradient
image defogging
transmittance
