基于边缘保持的自适应高斯衰减去雾算法

杨燕; 张国强; 李一菲; 岳辉

doi:doi:10.3788/lop55.081004

激光与光电子学进展, 2018, 55 (8): 081004, 网络出版: 2018-08-13

基于边缘保持的自适应高斯衰减去雾算法下载： 601次

Adaptive Gaussian Attenuation Defogging Algorithm Based on Edge Preservation

论文大纲

杨燕 ^*张国强李一菲岳辉

作者单位

兰州交通大学电子与信息工程学院, 甘肃兰州730070

图像处理高斯衰减边缘检测大气幕亮度边缘保持 image processing Gaussian attenuation edge detection luminance of the sky edge preservation

AI 词云图 AI语音精读 AI语音超短摘要

注：本部分内容由 AI 自动生成，请您知悉。

摘要

针对通过局部相似假设估计透射率，景深突变边缘出现Halo效应问题，提出一种基于边缘保持的自适应高斯衰减图像去雾算法。该方法从大气散射模型出发，引入大气幕亮度，将场景透射率的估计等效为大气幕亮度的估计。通过边缘检测算子提取边缘信息，分离边缘区域与非边缘区域，利用邻域内像素点的空间邻近度构建自适应高斯函数对非边缘区域进行平滑衰减，从而获得最优效果。通过大量实验对所提方法进行验证，结果表明复原的图像整体平滑，细节明显，有效地消除景深突变处的Halo效应,并且在客观评价中也体现出了优势。

Abstract

An adaptive Gaussian attenuation image defogging algorithm based on edge preservation is proposed to solve the problem when transmissivity is estimated under the local similarity assumption, the abrupt change edge of depth field often leads to the Halo effect .This method starts from the atmospheric scattering model and introduces the luminance of the sky, the estimation of scene transmissivity can be equivalent to the estimation of the luminance of the sky. The edge information is extracted by the edge detection operator to separate the edge region and the non-edge region. The adaptive Gaussian function is constructed by using the spatial proximity of the pixel points in the neighborhood to smooth and attenuation on the non-edge region, thereby obtaining the optimal effect. Through a large number of experiments to verify the proposed method, the results show that the restored image is smooth and the details are obvious. It can effectively eliminate the Halo effects at the abrupt changes of the depth field, and it also shows its advantages in the objective evaluation.

参考文献

[1] He L, Zhao J, Zheng N, et al. Haze removal using the difference-structure-preservation prior［J］. IEEE Transactions on Image Processing, 2017, 26(3): 1063-1075.

[2] 杨燕, 陈高科. 基于光补偿和逐像素透射率的图像复原算法［J］. 通信学报, 2017, 38(5): 48-56.

Yang Y, Chen G K. Single image visibility restoration algorithm using optical compensation and pixel-by-pixel transmission estimation［J］. Journal on Communications, 2017,38(5): 48-56.

[3] He K, Sun J, Tang X. Single image haze removal using dark channel prior［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(12): 2341-2353.

[4] Meng G, Wang Y, Duan J, et al. Efficient image dehazing with boundary constraint and contextual regularization［C］. 2013 IEEE International Conference on Computer Vision, 2013: 617-624.

[5] Sun W, Wang H, Sun C, et al. Fast single image haze removal via local atmospheric light veil estimation［J］. Computers & Electrical Engineering, 2015, 46(C): 371-383.

[6] Zhu Q, Mai J, Shao L. A fast single image haze removal algorithm using color attenuation prior［J］. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3533.

[7] Cai B, Xu X, Jia K, et al. Dehaze Net: an end-to-end system for single image haze removal［J］. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198.

[8] Ancuti C O, Ancuti C. Single image dehazing by multi-scale fusion［J］. IEEE Transactions on Image Processing, 2013, 22(8): 3271-3282.

[9] Tarel J P, Hautiere N. Fast visibility restoration from a single color or gray level image［C］. 2009 IEEE 12th International Conference onComputer Vision, 2009: 2201-2208.

[10] Jin W, Mi Z, Wu X, et al. Single image de-haze based on a new dark channel estimation method［C］. 2012 IEEE International Conference on Computer Science and Automation Engineering, 2012: 791-795.

[11] He K, Sun J, Tang X. Guided imagefiltering［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(6): 1397-1409.

[12] Tai S C, Yang S M. A fast method for image noise estimation using Laplacian operator and adaptive edge detection［C］. 2008 IEEE International Symposium on Communications, Control and Signal Processing, 2008: 1077-1081.Sulami M, Glatzer I, Fattal R, et al. Automatic recovery of the atmospheric light in hazy images［C］. 2014 IEEE International Conference on Computational Photography, 2014: 1-11.

[13] Wang Z, Bovik A C, Sheikh H R. Structural similarity based image quality assessment［J］. Digital Video Image Quality & Perceptual Coding, 2005.

[14] Hautière N, Tarel J P, Aubert D, et al. Blind contrast enhancement assessment by gradient ratioing at visible edges［J］. Image Analysis & Stereology Journal, 2008, 27(2): 87-95.

[15] Xu Y, Wen J, Fei L, et al. Review of video and image defogging algorithms and related studies on image restoration and enhancement［J］. IEEE Access, 2016, 4: 165-188.

杨燕, 张国强, 李一菲, 岳辉. 基于边缘保持的自适应高斯衰减去雾算法[J]. 激光与光电子学进展, 2018, 55(8): 081004. Yang Yan, Zhang Guoqiang, Li Yifei, Yue Hui. Adaptive Gaussian Attenuation Defogging Algorithm Based on Edge Preservation[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081004.

基于边缘保持的自适应高斯衰减去雾算法下载： 601次

关于本站 Cookie 的使用提示

全站搜索

基于边缘保持的自适应高斯衰减去雾算法 下载： 601次

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索

基于边缘保持的自适应高斯衰减去雾算法下载： 601次