CAJ | 학술논문

矿井成像条件较为复杂,导致视频监控系统所获取的图像往往具有对比度低、且参杂大量随机噪声的特点,给实时监控矿井生产状况带来了不便. 为此,采用灰色关联度法改进非局部均值滤波算法,提出了一种矿井视频监控图像改进非局部均值滤波算法. 该算法首先对原始矿井视频监控图像采用均值滤波算法进行预处理,得到预滤波图像,分别对原始矿井视频监控图像和预滤波图像划分为5×5大小的图像块,将该2幅图像中对应位置图像块的像素点灰度值集合分别记为待比较序列和参考序列,计算其灰色关联度值,将较小的灰色关联度值对应的原始矿井视频监控图像中的图像块标记为疑似噪声图像块;其次对每个疑似噪声图像块分别统计其像素灰度极大、极小值,并将该类像素点标记为噪声点;然后以每个噪声点为中心取大小为3×3的图像块,进行非局部均值滤波;最后对滤波后的矿井视频监控图像采用直方图均衡化方法进行对比度拉伸,改善图像的视觉效果. 试验结果表明:本研究算法无需对图像中每个像素点灰度值进行逐一滤波,提高了图像处理效率,有助于实现矿井视频监控图像的高效处理.
광정성상조건교위복잡,도치시빈감공계통소획취적도상왕왕구유대비도저、차삼잡대량수궤조성적특점,급실시감공광정생산상황대래료불편. 위차,채용회색관련도법개진비국부균치려파산법,제출료일충광정시빈감공도상개진비국부균치려파산법. 해산법수선대원시광정시빈감공도상채용균치려파산법진행예처리,득도예려파도상,분별대원시광정시빈감공도상화예려파도상화분위5×5대소적도상괴,장해2폭도상중대응위치도상괴적상소점회도치집합분별기위대비교서렬화삼고서렬,계산기회색관련도치,장교소적회색관련도치대응적원시광정시빈감공도상중적도상괴표기위의사조성도상괴;기차대매개의사조성도상괴분별통계기상소회도겁대、겁소치,병장해류상소점표기위조성점;연후이매개조성점위중심취대소위3×3적도상괴,진행비국부균치려파;최후대려파후적광정시빈감공도상채용직방도균형화방법진행대비도랍신,개선도상적시각효과. 시험결과표명:본연구산법무수대도상중매개상소점회도치진행축일려파,제고료도상처리효솔,유조우실현광정시빈감공도상적고효처리.
The mine imaging condition is relatively complex,which results that the images obtained by the video monito-ring system has the characteristics of low contrast and mixed with large number of random noise,which brought inconvenience to monitoring the mine production conditions in real-time. So,the grey correlation method is adopted to improve the non-local means filtering algorithm,and an improved non-local means filtering algorithm of mine video monitoring image is proposed. Firstly,the original mine video monitoring image is processed by the average filtering algorithm to obtain the filtering image,and the original mine video monitoring image and filtering image are divided into images blocks with the size of 5×5,the grey value collections of the pixies in the image bocks with the corresponding position in the above two images are regarded as the com-pare sequence and reference sequence respectively to calculate the grey correlation value of the compare sequence and refer-ence sequence,and the image blocks with smaller grey correlation values in the original mine video monitoring image can be marked with suspected noise image blocks;secondly,the maximum and minimum grey values of the pixels in the suspected noise image blocks are marked as noise pixels;then,the image blocks with the size of 3×3 centered with the noise pixels are processed with the non-local means filtering algorithm;finally,the contrast of mine video monitoring image after filtering can be stretched by using the histogram equalization method to improve the image visual effects. The experimental results show that the algorithm proposed in this paper dose not need to filter the noise pixels one by one,therefore,the filtering efficiency of image processing is improved. It contributes to realize the goal of processing the mine video monitoring image with high efficiency.