基于分类和模糊滤波的X光图像椒盐噪声滤除算法
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摘要
为了解决当前椒盐噪声滤除算法对X光图像滤除效果不佳且运算效率不高的问题,提出了一种融合多级分类和自适应模糊滤波的椒盐噪声滤除方法,主要包括像素点多级分类和自适应模糊滤波两个部分。在像素点多级分类阶段,先结合先验知识设计快速的一级粗分类,将像素点分为椒盐噪声、信号和可疑噪声三类。对于可疑噪声,再提取区域内的直方图分布特征,设计BP神经网络分类器进行精确分类,最终将图像中的所有像素点分为信号和椒盐噪声两类。在自适应模糊滤波阶段,针对三种模糊集合分别创建模糊隶属度函数,计算模糊隶属度值,通过模糊加权求和恢复像素点亮度。实验结果表明,该方法的像素点分类正确率高,滤波后图像的峰值信噪比高,平均滤波耗时少。
In order to solve the problem that the current salt and pepper noise filtering algorithms for X-ray images are ineffective and low efficiency,this paper proposed a salt and pepper filtering algorithm with multi-level classification and adaptive fuzzy filtering,which included two parts,such as pixels' multi-level classification and adaptive fuzzy filtering. In the process of multi-level classification,it designed a rapid rough classification according to priori knowledge,to divide the pixels into three categories: salt and pepper noise,signal and suspicious noise. For the suspicious noise,it extracted the histogram distribution features in the region,and designed the BP neural network classifier to classify the pixels,and finally classified all the pixels in the image into two kinds of signal and salt and pepper noise. In the process of adaptive fuzzy filtering,it created fuzzy membership function for three fuzzy sets,and calculated fuzzy membership value,and restored pixel brightness by fuzzy weighted summing. The experimental results show that the new method has high accuracy of pixel classification,high peak signal to noise ratio of filtered image,and less average time-consuming of filtering.
In order to solve the problem that the current salt and pepper noise filtering algorithms for X-ray images are ineffective and low efficiency,this paper proposed a salt and pepper filtering algorithm with multi-level classification and adaptive fuzzy filtering,which included two parts,such as pixels' multi-level classification and adaptive fuzzy filtering. In the process of multi-level classification,it designed a rapid rough classification according to priori knowledge,to divide the pixels into three categories: salt and pepper noise,signal and suspicious noise. For the suspicious noise,it extracted the histogram distribution features in the region,and designed the BP neural network classifier to classify the pixels,and finally classified all the pixels in the image into two kinds of signal and salt and pepper noise. In the process of adaptive fuzzy filtering,it created fuzzy membership function for three fuzzy sets,and calculated fuzzy membership value,and restored pixel brightness by fuzzy weighted summing. The experimental results show that the new method has high accuracy of pixel classification,high peak signal to noise ratio of filtered image,and less average time-consuming of filtering.
引文
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[8]王义,王江云,宋晓,等.基于模糊转换的图像椒盐噪声检测和去除[J].电子测量与仪器学报,2017,31(4):211-216.(Wang Yi,Wang Jiangyun,Song Xiao,et al. Image salt-and pepper-noise detection and removal based on fuzzy switching[J]. Journal of Electronic Measurement and Instrumentation,2017,31(4):211-216.)
[9] Roy A,Singha J,Devi S S,et al. Impulse noise removal using SVM classification based fuzzy filter from gray scale images[J]. Signal Processing,2016,12(8):262-273.
[10] Gómezmoreno H,Giljiménez P,Lafuentearroyo S,et al. A“salt and pepper”noise reduction scheme for digital images based on support vector machines classification and regression[J]. The Scientific World Journal,2014,20(14):826405.
[11]Wu Bing,Han Shaojun,Xiao Jin,et al. Error compensation based on BP neural network for airborne laser ranging[J]. Optik-International Journal for Light and Electron Optics,2016,127(8):4083-4088.
[2] Karthik B,Kumar T K,Dorairangaswamy M A,et al. Removal of high density salt and pepper noise through modified cascaded filter[J]. Middle East Journal of Scientific Research,2014,20(10):1222-1228.
[3] Lu C T,Chou T C. Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter[J]. Pattern Recognition Letters,2012,33(10):1287-1295.
[4] Esakkirajan S,Veerakumar T,Subramanyam A N,et al. Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter[J]. IEEE Signal Processing Letters,2013,18(5):287-290.
[5]王贵君,杨永强.基于高概率椒盐噪声的模糊滤波器在图像恢复中的算法设计[J].电子学报,2015,43(1):24-29.(Wang Guijun,Yang Yongqiang. Algorithm design of fuzzy filter based on saltand pepper-noise with high probability in image restoration[J]. Acta Electronica Sinica,2015,43(1):24-29.)
[6]白中浩,焦英豪,白芳华.基于主动形状模型及模糊推理的驾驶疲劳检测[J].仪器仪表学报,2015,36(4):768-775.(Bai Zhonghao,Jiao Yinghao,Bai Fanghua. Driving fatigue detection based on active shape model and fuzzy inference[J]. Chinese Journal of Scientific Instrument,2015,36(4):768-775.)
[7]江巨浪,章瀚,朱柱,等.高密度椒盐噪声的多方向加权均值滤波[J].计算机工程与应用,2016,52(6):204-208.(Jiang Julang,Zhang Han,Zhu Zhu,et al. Polydirectional weighted mean filter for high-density salt&pepper noise[J]. Computer Engineering and Applications,2016,52(6):204-208.)
[8]王义,王江云,宋晓,等.基于模糊转换的图像椒盐噪声检测和去除[J].电子测量与仪器学报,2017,31(4):211-216.(Wang Yi,Wang Jiangyun,Song Xiao,et al. Image salt-and pepper-noise detection and removal based on fuzzy switching[J]. Journal of Electronic Measurement and Instrumentation,2017,31(4):211-216.)
[9] Roy A,Singha J,Devi S S,et al. Impulse noise removal using SVM classification based fuzzy filter from gray scale images[J]. Signal Processing,2016,12(8):262-273.
[10] Gómezmoreno H,Giljiménez P,Lafuentearroyo S,et al. A“salt and pepper”noise reduction scheme for digital images based on support vector machines classification and regression[J]. The Scientific World Journal,2014,20(14):826405.
[11]Wu Bing,Han Shaojun,Xiao Jin,et al. Error compensation based on BP neural network for airborne laser ranging[J]. Optik-International Journal for Light and Electron Optics,2016,127(8):4083-4088.