液晶与显示, 2020, 35 (2): 167, 网络出版: 2020-03-26   

基于连通性检测的图像椒盐噪声滤波算法

Salt and pepper noise filtering algorithm based on connectivity detection
作者单位
四川轻化工大学 自动化与信息工程学院, 四川 宜宾 644005
摘要
为了在滤除图像椒盐噪声的同时保护图像边缘细节, 提出了一种基于连通性检测的图像椒盐噪声滤波算法。由于椒盐噪声点的灰度值与正常像素点的灰度值相比往往存在较大差异, 本算法先通过比较像素点灰度值与其邻域像素点灰度值, 将差异较大的像素点列为疑似噪声点, 然后通过检测疑似噪声点是否是图像连通区域的一部分来判断该点是否是噪声点, 最后通过中值滤波器将噪声点滤除。该算法可以有效区分图像区域边缘与椒盐噪声。实验结果表明, 该算法可以有效去除密度范围从0~0.9的椒盐噪声, 在0.9的噪声密度下, 算法的峰值信噪比仍可达到30 dB。满足有效去除不同密度范围的椒盐噪声的同时保护图像细节的要求。
Abstract
In order to protect the image edge details while filtering out the image salt and pepper noise, an image salt and pepper noise filtering algorithm based on connectivity detection is proposed. Since the gray value of the salt and pepper noise point tends to be different from the gray value of the normal pixel point, the algorithm compares the pixel point gray value with the gray value of the neighborhood pixel point to make the pixel point with larger difference. It is listed as a suspected noise point, and then it is judged whether the point is a noise point by detecting whether the suspected noise point is a part of the image connected area. The noise point is finally filtered out by the median filter. The algorithm can effectively distinguish the edge of the image area from the salt and pepper noise. The experimental results show that the algorithm can effectively remove the salt and pepper noise with the density ranging from 0 to 0.9. The peak signal-to-noise ratio of the algorithm can still reach 30 dB with the noise density of 0.9. It satisfies the requirement to effectively remove the salt and pepper noise of different density ranges while protecting the image details.
参考文献

[1] SINGH V, DEV R, DHAR N K, et al. Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images [J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5): 3170-3176.

[2] MA C B, LV X W, AO J. Difference based median filter for removal of random value impulse noise in images [J]. Multimedia Tools and Applications, 2019, 78(1): 1131-1148.

[3] 王红梅, 李言俊, 张科.基于极值检测的图像滤波算法[J].激光与红外, 2007, 37(10): 1117-1119.

    WANGH M, LI Y J, ZHANG K. An image filtering algorithm based on extremum detection [J]. Laser & Infrared, 2007, 37(10): 1117-1119. (in Chinese)

[4] SHI Z H, LI Y W, ZHANG C Q, et al. Weighted median guided filtering method for single image rain removal [J]. EURASIP Journal on Image and Video Processing, 2018, 2018(1): 35.

[5] YANG R K, YIN L, GABBOUJ M, et al. Optimal weighted median filtering under structural constraints [J]. IEEE Transactions on Signal Processing, 1995, 43(3): 591-604.

[6] 王拓, 王洪雁, 裴炳南.一种消除椒盐噪声的迭代自适应中值滤波算法[J].电光与控制, 2019, 26(2): 23-27.

    WANG T, WANG H Y,PEI B N. An iterative adaptive median filtering algorithm for salt and pepper noise removal [J]. Electronics Optics & Control, 2019, 26(2): 23-27. (in Chinese)

[7] ZHANG S R, LI X N, ZHANG C Y. Modified adaptive median filtering [C]//Proceedings of 2018 International Conference on Intelligent Transportation, Big Data & Smart City. Xiamen, China: IEEE, 2018.

[8] HWANG H, HADDAD R A. Adaptive median filters: new algorithms and results [J]. IEEE Transactions on Image Processing, 1995, 4(4): 499-502.

[9] LIN T C, YU P T.Adaptive two-pass median filter based on support vector machines for image restoration [J]. Neural Computation, 2004, 16(2): 333-354.

[10] ZHANG Z, HAN D Q, DEZERT J, et al. A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning [J]. Signal Processing, 2018, 147: 173-189.

[11] 郑亮, 方恩印, 朱明.基于开关二级检测的图像椒盐噪声滤波算法[J].液晶与显示, 2019, 34(1): 74-80.

    ZHENG L, FANG E Y, ZHU M. Salt and pepper noise filtering algorithms based on switch two-stage detection [J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(1): 74-80. (in Chinese)

[12] JAYARAJ V, EBENEZER D. A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in images [J]. EURASIP Journal on Advances in Signal Processing, 2010, 2010(1): 690218.

[13] 钟灵, 章云.对等组的快速开关型矢量中值滤波[J].计算机工程, 2010, 36(22): 20-21, 25.

    ZHONG L, ZHANG Y. Ultra-fast switching vector median filtering of peer group [J]. Computer Engineering, 2010, 36(22): 20-21, 25. (in Chinese)

[14] 苟中魁, 张少军, 李忠富, 等.一种基于极值的自适应中值滤波算法[J].红外与激光工程, 2005, 34(1): 98-101.

    GOU Z K, ZHANG S J, LI Z F, et al. New adaptive median filter algorithm based on extreme value [J]. Infrared and Laser Engineering, 2005, 34(1): 98-101. (in Chinese)

[15] 黄文笔, 战荫伟, 陈家益, 等.改进的自适应中值滤波算法[J].计算机系统应用, 2018, 27(10): 183-188.

    HUANG W B, ZHAN Y W, CHENJ Y, et al. Improved adaptive median filtering algorithm [J]. Computer Systems & Applications, 2018, 27(10): 183-188. (in Chinese)

[16] 唐彩虹, 蔡利栋.一种基于直方图的加权均值滤波方法[J].微计算机信息, 2006, 22(13): 202-204.

    TANG C H, CAI L D. A weighted mean filtering algorithm based on histogram [J]. Microcomputer Information, 2006, 22(13): 202-204. (in Chinese)

[17] SHANG X W, LIANG J, WANG G Z, et al. Color-sensitivity-based combined PSNR for objective video quality assessment [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(5): 1239-1250.

[18] HOR A, ZIOU D. Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure? [J]. IET Image Processing, 2013, 7(1): 12-24.

[19] 闫歌, 许廷发, 马旭,等. 动态测量的高光谱图像压缩感知 [J]. 中国光学, 2018, 11(4): 550-559.

    YAN G, XU T F, MA X, et al. Hyperspectral image compression sensing based on dynamic measurement [J]. Chinese Optics, 2018, 11(4): 550-559. (in Chinese)

[20] 何阳, 黄玮, 王新华,等. 稀疏阈值的超分辨率图像重建 [J]. 中国光学, 2016, 9(5): 532-539.

    HE Y, HUANG W, WANG X H, et al. Super-resolution image reconstruction based on sparse threshold [J]. Chinese Optics, 2016, 9(5): 532-539. (in Chinese)

马逸东, 周顺勇. 基于连通性检测的图像椒盐噪声滤波算法[J]. 液晶与显示, 2020, 35(2): 167. MA Yi-dong, ZHOU Shun-yong. Salt and pepper noise filtering algorithm based on connectivity detection[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(2): 167.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!