Zhenxing Xu, Ping Yang, Bing Xu. An enhancement algorithm for low signal noise ratio of point pixel[J]. Opto-Electronic Engineering, 2017, 44(11): 1083-1088. doi: 10.3969/j.issn.1003-501X.2017.11.007
Citation: Zhenxing Xu, Ping Yang, Bing Xu. An enhancement algorithm for low signal noise ratio of point pixel[J]. Opto-Electronic Engineering, 2017, 44(11): 1083-1088. doi: 10.3969/j.issn.1003-501X.2017.11.007

An enhancement algorithm for low signal noise ratio of point pixel

More Information
  • Both the spatial or time domain processing all have certain limitations by using only the partial information of the target. Therefore, this paper proposes an enhancement algorithm for low signal noise ratio point pixel based on time and spatial correlation. First, the improved Top-hat transform is used for image spatial processing, and then the wavelet transform, which has a good advantage of frequency analysis into the image, is introduced into the time domain processing. Finally, the joint probability distribution is adopted to integrate gray image obtained by the two processing methods. The experimental results show that the average grey value and SNR gain of target are enhanced effectively after enhancement.
  • 加载中
  • [1] Bai Xiangzhi, Zhou Fugen. Infrared small target enhancement and detection based on modified top-hat transformations[J]. Computers and Electrical Engineering, 2010, 36(10): 1193‒1201.

    Google Scholar

    [2] 张耀, 雍杨, 张启衡, 等.低对比度小目标检测[J].强激光与粒子束, 2010, 22(11): 2566‒2570.

    Google Scholar

    Zhang Yao, Yong Yang, Zhang Qiheng, et al. Detection of dim point target with low contrast[J]. Laser and Particle Beams, 2010, 22(11): 2566‒2570.

    Google Scholar

    [3] 张路, 张志勇, 肖山竹, 等.基于多向背景预测的红外弱小目标检测[J].信号处理, 2010, 26(11): 1646‒1651. doi: 10.3969/j.issn.1003-0530.2010.11.008

    CrossRef Google Scholar

    Zhang Lu, Zhang Zhiyong, Xiao Shanzhu, et al. Detection of dim infrared targets by multi-direction prediction of background[J]. Journal of Signal Processing, 2010, 26 (11): 1646‒1651. doi: 10.3969/j.issn.1003-0530.2010.11.008

    CrossRef Google Scholar

    [4] Fan Xiangsuo, Xu Zhiyong, Zhang Jianlin, et al. Infrared dim and small targets detection method based on local energy center of sequential image[J]. Mathematical Problems in Engineering, 2017, 2007: 4572147. doi: 10.1155/2017/4572147.

    CrossRef Google Scholar

    [5] 景亮, 彭真明, 何艳敏, 等.各向异性SUSAN滤波红外弱小目标检测[J].强激光与粒子束, 2013, 25(9): 124‒129.

    Google Scholar

    Jiang Liang, Peng Zhenming, He Yanmin, et al. Infrared dim target detection based on anisotropic SUSAN filtering[J]. High Power Laser and Particle Beams, 2013, 25(9): 124‒129.

    Google Scholar

    [6] Fan Xiangsuo, Xu Zhiyong, Zhang Jianlin, et al. Dim small targets detection based on self-adaptive caliber temporal-spatial filtering[J]. Infrared Physics & Technology, 2017, 85: 465‒477.

    Google Scholar

    [7] 荣健, 申金娥, 钟晓春.基于小波和SVR的红外弱小目标检测方法[J].西南交通大学学报, 2008, 43(5): 555‒560.

    Google Scholar

    Rong Jian, Shen Jine, Zhong Xiaochun. New method for infrared dim target detection based on wavelet and SVR[J]. Journal of Southwest Jiaotong University, 2008, 43(5): 555‒560.

    Google Scholar

    [8] Liu Gang, Liang Xiaogeng. Detection of aerial small target in infrared image based on wavelet transform and pipe-line filter[J]. Computer Engineering and Applications, 2011, 47(30): 198‒201.

    Google Scholar

    [9] 李大伟. 复杂背景下红外弱小目标检测[D]. 哈尔滨: 哈尔滨工业大学, 2013.

    Google Scholar

    Li Dawei. Small dim targets detection in infrared video with complex background [D]. Harbin: Harbin Institute of Technology, 2013.

    Google Scholar

    [10] Bae T W, Kim Y C, Ahn S H, et al. An efficient two-dimensional least mean square (TDLMS) based on block statistics for small target detection[J]. Journal of Infrared, Millimeter, and Tera-hertz Waves, 2009, 30(10): 1092–1101. doi: 10.1007/s10762-009-9530-6

    CrossRef Google Scholar

    [11] 佟雨兵, 张其善, 祁云平, 等.基于PSNR与SSIM联合的图像质量评价模型[J].中国图象图形学报, 2010, 11(12): 1758–1763.

    Google Scholar

    Tong Yubing, Zhang Qishan, Qi Yunping, et al. Image quality assessing by combining PSNR with SSIM[J]. Chinese Journal of Image and Graphics, 2010, 11(12): 1758–1763.

    Google Scholar

    [12] Wang Zhou, Bovik A C, Sheikh H R, et al. Image quality as-sessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612.

    Google Scholar

    [13] 曹琦, 毕笃彦.红外弱小目标检测中的特征选择性滤波方法[J].光学学报, 2011, 29(9): 2048‒2412.

    Google Scholar

    Cao Qi, Bi Duyan. Characteristic-selecting filtering in infrared small target detection[J]. Acta Optica Sinica, 2011, 29(9): 2048–2412.

    Google Scholar

  • Abstract: Point pixel enhancement algorithm is an important preprocessing technique in automatic point recognition and detection systems. The point pixel in the image of the pixels are small, lacking information such as shape and texture, of low signal noise ratio, and easily submerged in the complex background, which is extremely unfavorable for subsequent extraction and detection. So point enhancement is necessary.

    In recent years, lots of papers have been published about the use of spatial domain or time domain on single-frame image enhancement method. Spatial domain is processed to achieve background prediction and the purpose of enhancing the point by filtering or spatial correlation, such as top-hat background suppression, local adaptive filtering and two-dimensional least mean square filtering method (TDLMS). At present, some researchers have proposed a background modeling of anisotropy method from the perspective of image "singularity" for large span background. And time domain processing mainly uses wavelet transform to realize point pixel enhancement, calculate the normalized correlation coefficient among the point pixel coefficient, the background edge coefficient and the noise figure, and enhance the point pixel by distinguishing the correlation coefficient difference, suppressing the background.

    The above-mentioned enhancement methods, whether they are spatial domain or time domain processing, use only part of the information of the point pixels. Thus they all have limitations: The spatial processing focuses on the use of gray features of the pixel, and the disadvantage is to ignore the point pixel like the continuity of gray in the time domain. The time domain processing focuses on the continuity of the point pixel gray scale in the time domain, but does not fully consider the gray-scale distribution of the point pixel in the spatial domain. The interference of the noise may introduce more false information. At present, the multi-frame point pixel enhancement method of joint space-time domain is paid more and more attention to. Based on the difference among the point pixel, the background and the noise in the space-time characteristics, we fully consider the spatial and temporal characteristics of the point pixel and uses the joint distribution probability to fuse the gray-scale graphs obtained by the two methods of space-time domain, to improve the image signal-to-noise ratio.

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(5)

Tables(7)

Article Metrics

Article views(5864) PDF downloads(3448) Cited by(0)

Access History

Other Articles By Authors

Article Contents

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint