基于DWT与改进中值滤波的矿井视频监控图像去噪

井下光照不均、煤尘浓度大以及视频图像获取设备电路电压不稳定等各类因素的存在,导致矿井视频监控系统获取的图像存在大量噪声,影响了对矿井各类生产信息的准确判读。为此,将离散小波变换(Discrete wavelet transform,DWT)与改进中值滤波算法相结合,提出了一种矿井视频监控图像高效去噪算法。首先,对获取的矿井视频图像进行自适应噪声检测,根据检测结果,对图像采用改进中值滤波算法处理;然后对滤波后的图像进行3层离散小波变换,鉴于图像的噪声信息绝大部分集中分布于高频分解系数中,故对低频分解系数不作处理;最后对高频分解系数采用一种改进软阈值去噪函数模型进行去噪,将去噪后的高频分解系数与原始低频分解系数进行重构,得到去噪后清晰度较高的图像。采用实地获取的山西潞安某煤矿井下视频图像进行试验,并与小波软阈值去噪、中值滤波等算法进行去噪效果对比分析,此外,对各算法的试验结果分别采用信噪比(Signal noise ratio,SNR)以及算法运行时间进行评价,结果表明:新算法对于矿井视频监控图像的去噪效果优于其余2类算法,且算法运算时间也具有一定的优势。

Mine Video Surveilance Image Algorithm Based on DWT and Improved Median Filtering Algorithm

The existing factors of uneven illumination,coal dust and circuit voltage instability of video surveilance image acquistition devices,resulting in a lot of noises are existed in video surveilance image,the accurate interpretation of mine all kinds of production information is affected. Combined with discrete wavelet transform ( DWT) and improved median filtering al-gorithm,a filtering algorithm of mine video surveilance with high efficiency is proposed. Firstly,according to the distribution characteristics of the noise in mine video surveilance image,the adaptive noise detection operator is proposed,according to the noise detection results,the improved median filtering algorithm is adopted to filtering out the salt&pepper noise in mine video surveilance image;then,the filtered image is conducted three-layers discrete wavelet transform,the high-frequency and low-fre-quency coefficients are obtained,the gaussian noise is not distributed in low-frequency coefficients,most of the gaussian noise is distributed in high-frequency coefficients,so,the low-frequency coefficients can be unchanged;finally,the a improved wavelet thresholding filtering function model is proposed to filtering out the gaussion noise in high-frequency coefficients,the filtered high-frequency coefficients and original low-frequency coefficients are reconstructed,the high resoulution image after denoising is obtained. The mine video surveilance images of a mine of Lu'an city,Shanxi province are obtained,the filtering effects of wavelet thresholding filtering function model,median filtering algorithm and the algorithm proposed in paper are analyzed,be-sides that,the indicators of signal noise ratio ( SNR) and algorithm operation time are adopted to evaluation the effects of the a-bove algorithms,the results show that,the performance of the algorithm proposed in this paper is better than others,besidest-hat,the operation time of the algorithm proposed in this paper is also shorter than others.