Abstract: The mine imaging condition is relatively complex，which results that the images obtained by the video monitoring 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 compare sequence and reference sequence respectively to calculate the grey correlation value of the compare sequence and reference 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.

Key words: Mine video monitoring system, Grey correlation, Non-local means filtering, Average filtering, Image block, Histogram equalization