结合暗亮通道先验的远近景融合去雾算法

高涛; 刘梦尼; 陈婷; 王松涛; 蒋硕

doi:10.7652/xjtuxb202110009

您当前的位置：

首页 >

文章列表页 >

结合暗亮通道先验的远近景融合去雾算法

更新时间：2025-07-09

- 结合暗亮通道先验的远近景融合去雾算法
- A Far and Near Scene Fusion Defogging Algorithm Based on the Prior of Dark-Light Channel
- 西安交通大学学报 2021年55卷第10期页码：78-86
- 作者机构：
  
  长安大学信息工程学院,西安,710000
- 作者简介：
- 基金信息：
  
  : 2021-03-03。作者简介: 高涛(1980—),男,教授,博士生导师。基金项目: 国家重点研发计划资助项目(2019YFE0108300);国家自然科学基金资助项目(62001058);陕西省重点研发计划资助项目(2019GY-039)
- DOI：10.7652/xjtuxb202110009
  中图分类号： TP391
- 网络首发：2021-10-10，
  
  纸质出版：2021
- 稿件说明：
移动端阅览
高涛, 刘梦尼, 陈婷, 等. 结合暗亮通道先验的远近景融合去雾算法[J]. 西安交通大学学报, 2021,55(10):78-86.

A Far and Near Scene Fusion Defogging Algorithm Based on the Prior of Dark-Light Channel[J]. 2021, 55(10): 78-86.
高涛, 刘梦尼, 陈婷, 等. 结合暗亮通道先验的远近景融合去雾算法[J]. 西安交通大学学报, 2021,55(10):78-86. DOI： 10.7652/xjtuxb202110009.

A Far and Near Scene Fusion Defogging Algorithm Based on the Prior of Dark-Light Channel[J]. 2021, 55(10): 78-86. DOI： 10.7652/xjtuxb202110009.

摘要

为解决传统暗通道不适用于大面积天空区域

容易造成去雾图像失真的问题

提出一种结合暗亮通道先验的远近景融合去雾算法。首先

利用改进的二维Otsu图像分割算法

混合近景和远景区域的暗通道

并基于最优的客观质量评价指标对近景和远景区域设置混合暗通道的自适应调节参数; 其次

针对真实物理场景中大气光并非均匀不变常量的问题

建立暗亮通道融合模型

并计算大气光图; 为了提升处理速度

在不降低恢复质量的前提下

选取与原图对应的灰度图作为引导图像对透射率图进行细化; 最后

采用基于视觉感知的亮度/颜色补偿模型对图像修正

提高了复原图像的对比度和色彩饱和度。实验结果表明

所提算法在主观和客观角度均取得最好的效果

其中客观指标PSNR在数值上比He的算法平均高出24.04%。由此得出

通过所提算法复原的图像更加清晰、细节信息和结构更加明显

更适于人眼的观察

验证了算法的有效性。

Abstract

A far and near scene fusion defogging algorithm based on the prior of dark-light channel is proposed to solve the problem that traditional dark channel is not suitable for large sky area and it is easy to cause the distortion of dehazed image. Firstly

an improved two-dimensional Otsu image segmentation algorithm is utilized to mix the dark channels in the close and distant areas

and the adaptive adjustment parameters of the mixed dark channels are calculated based on the optimal objective quality evaluation index for the close and distant areas. Secondly

aiming at the problem that atmospheric light is not uniform and constant in real physical scenes

a dark-light channel fusion model is established to calculate the atmospheric light map. Furthermore

in order to improve processing speed

the grayscale image corresponding to the original image is selected as a guide image to refine the transmittance image without reducing restoration quality. Finally

the brightness/colour compensation model based on visual perception is used for image correction to improve the contrast and colour saturation of the restored image. Experimental results show that the proposed algorithm achieves the best results from both subjective and objective perspectives

in which the objective index PSNR is 24.04% higher than that of He's algorithm on average. It is concluded that the image recovered by the proposed algorithm is clearer

with more obvious details and structure

and is more suitable for human eyes to observe

which verifies the effectiveness of the algorithm.

关键词

Keywords

references

杨燕, 陈高科, 周杰. 基于高斯权重衰减的迭代优化去雾算法 [J]. 自动化学报, 2019, 45(4): 819-828.

YANG Yan, CHEN Gaoke, ZHOU Jie. Iterative optimization defogging algorithm using Gaussian weight decay [J]. Acta Automatica Sinica, 2019, 45(4): 819-828.

黄文君, 李杰, 齐春. 低秩与字典表达分解的浓雾霾场景图像去雾算法 [J]. 西安交通大学学报, 2020, 54(4): 118-125.

HUANG Wenjun, LI Jie, QI Chun. A defogging algorithm for dense fog images via low-rank and dictionary expression decomposition [J]. Journal of Xi'an Jiaotong University, 2020, 54(4): 118-125.

杨燕, 陆鑫璇. 结合自适应亮度变换不等式估计透射率的图像去雾方法 [J]. 西安交通大学学报, 2021, 55(6): 69-76.

YANG Yan, LU Xinxuan. An image dehazing method combining adaptive brightness transformation inequality to estimate transmittance [J]. Journal of Xi'an Jiaotong University, 2021, 55(6): 69-76.

LIU Yuhong, YAN Hongmei, GAO Shaobing, et al. Criteria to evaluate the fidelity of image enhancement by MSRCR [J]. IET Image Processing, 2018, 12(6): 880-887.

ZHANG Weidong, DONG Lili, PAN Xipeng, et al. Single image defogging based on multi-channel convolutional MSRCR [J]. IEEE Access, 2019, 7: 72492-72504.

HE Kaiming, SUN Jian, TANG Xiaoou. Single image haze removal using dark channel prior [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.

HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.

XU Yueshu, GUO Xiaoqiang, WANG Haiying, et al. Single image haze removal using light and dark channel prior [C]∥2016 IEEE/CIC International Conference on Communications in China. Piscataway, NJ, USA: IEEE, 2016: 7636813.

LI Jiayuan, HU Qingwu, AI Mingyao. Haze and thin cloud removal via sphere model improved dark channel prior [J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 472-476.

WANG Fengping, WANG Weixing. Road extraction using modified dark channel prior and neighborhood FCM in foggy aerial images [J]. Multimedia Tools and Applications, 2019, 78(1): 947-964.

ENGIN D, GENC A, EKENEL H K. Cycle-dehaze: enhanced cycleGAN for single image dehazing [C]∥31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ, USA: IEEE, 2018: 938-946.

LI Jinjiang, LI Guihui, FAN Hui. Image dehazing using residual-based deep CNN [J]. IEEE Access, 2018, 6: 26831-26842.

ZHANG He, PATEL V M. Densely connected pyramid dehazing network [C]∥31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 3194-3203.

REN Wenqi, MA Lin, ZHANG Jiawei, et al. Gated fusion network for single image dehazing [C]∥31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 3253-3261.

CHEN Dongdong, HE Mingming, FAN Qingnan, et al. Gated context aggregation network for image dehazing and deraining [C]∥19th IEEE Winter Conference on Applications of Computer Vision. Piscataway, NJ, USA: IEEE, 2019: 1375-1383.

OTSU N. A threshold selection method from gray-level histograms [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.

刘健庄, 栗文青. 灰度图象的二维Otsu自动阈值分割法 [J]. 自动化学报, 1993, 19(1): 101-105.

LIU Jianzhuang, LI Wenqing. The automatic thresholding of gray-level pictures via two-dimensional Otsu method [J]. Acta Automatica Sinica, 1993, 19(1): 101-105.

TAREL J P, HAUTIÈRE N. Fast visibility restoration from a single color or gray level image [C]∥12th International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2009: 2201-2208.

HE Kaiming, SUN Jian, TANG Xiaoou. Single image haze removal using dark channel prior [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.

TUFAIL Z, KHURSHID K, SALMAN A, et al. Improved dark channel prior for image defogging using RGB and YCbCr color space [J]. IEEE Access, 2018, 6: 32576-32587.

谢立, 熊刚, 于波, 等. 基于暗通道先验的大气退化图像去雾新方法 [J]. 控制工程, 2020, 27(2): 207-211.

XIE Li, XIONG Gang, YU Bo, et al. A novel haze removal algorithm for atmospheric degraded image with dark channel prior [J]. Control Engineering of China, 2020, 27(2): 207-211.

余春艳, 林晖翔, 徐小丹, 等. 雾天退化模型参数估计与CUDA设计 [J]. 计算机辅助设计与图形学学报, 2018, 30(2): 327-335.

YU Chunyan, LIN Huixiang, XU Xiaodan, et al. Parameter estimation of fog degradation model and CUDA design [J]. Journal of Computer-Aided Design Computer Graphics, 2018, 30(2): 327-335.

范新南, 冶舒悦, 史朋飞, 等. 改进大气散射模型实现的图像去雾算法 [J]. 计算机辅助设计与图形学学报, 2019, 31(7): 1148-1155.

FAN Xinnan, YE Shuyue, SHI Pengfei, et al. An image dehazing algorithm based on improved atmospheric scattering model [J]. Journal of Computer-Aided Design Graphics, 2019, 31(7): 1148-1155.

肖进胜, 申梦瑶, 雷俊锋, 等. 基于生成对抗网络的雾霾场景图像转换算法 [J]. 计算机学报, 2020, 43(1): 165-176.

XIAO Jinsheng, SHEN Mengyao, LEI Junfeng, et al. Image conversion algorithm for haze scene based on generative adversarial networks [J]. Chinese Journal of Computers, 2020, 43(1): 165-176.

姚婷婷, 梁越, 柳晓鸣, 等. 基于雾线先验的时空关联约束视频去雾算法 [J]. 电子与信息学报, 2020, 42(11): 2796-2804.

YAO Tingting, LIANG Yue, LIU Xiaoming, et al. Video dehazing algorithm via haze-line prior with spatiotemporal correlation constraint [J]. Journal of Electronics Information Technology, 2020, 42(11): 2796-2804.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于直方图偏差约束的快速模糊C均值图像分割法

用于图像分割的局部区域能量最小化算法

递推人工蜂群的模糊划分熵多阈值分割算法

一种多源遥感图像分割的融合新策略

多尺度非参数化水平集的超声心动图分割