基于自适应阈值分割与透射率融合的图像去雾算法  被引量:7

Image Dehazing Algorithm Based on Adaptive Threshold Segmentation and Transmittance Fusion

在线阅读下载全文

作  者:向文鼎 杨平[1,2] 许冰[1,2] XIANG Wending;YANG Ping;XU Bing(Key Laboratory on Adaptive Optics,Chinese Academy of Sciences,Chengdu 610209,China;Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610209,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区:[1]中国科学院自适应光学重点实验室,成都610209 [2]中国科学院光电技术研究所,成都610209 [3]中国科学院大学,北京100049

出  处:《计算机工程》2019年第10期239-245,共7页Computer Engineering

基  金:四川省杰出青年基金(2012JQ0012)

摘  要:利用暗通道先验去雾算法处理包含大块亮白区域的图像时,存在图像失真的问题。为此,提出一种改进的的单幅图像去雾算法。利用基于图像子块平均灰度值和标准差的四叉树分解方法得到大气光估值,通过图像暗通道直方图分布自适应计算分割阈值,据此将图像划分为亮白区域与非亮白区域。根据图像的灰度分布数据计算权重因子,将其融入透射率以提高图像边缘的平滑度。在此基础上,应用大气散射模型恢复无雾图像。实验结果表明,该算法能够有效解决天空区域色彩失真的问题,所得图像视觉效果明亮自然,图像交界景深突变处也较为平滑。When the dark channel priori dehazing algorithm is used to process an image with a large bright white area,the problem of image distortion occurs.To this end,an improved single image dehazing algorithm is proposed.The atmospheric light is obtained by quad-tree decomposition method based on sub-block average gray value and standard deviation.The segmentation threshold is adaptively calculated by the image dark channel histogram distribution,and the image is divided into a bright area and a non-bright area.The grayscale distribution of the image is used to calculate a weighting factor.And this weighting factor is used to blend the transmittance to make the edges smoother.On this basis,the haze-free image is restored by the atmospheric scattering model.Experimental results show that the algorithm effectively solves the problem of color distortion in the sky region,the visual effect is bright and natural,and the sudden change of the depth of the image boundary is smooth.

关 键 词:图像去雾 阈值分割 四叉树分解 大气散射模型 引导滤波 

分 类 号:TP751[自动化与计算机技术—检测技术与自动化装置]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象