Abstract: The existing factors of inadequate underground light source，mechanical vibration and a large number of coal dusts make the real-time images obtained by the mine video monitoring system are fuzzy and mixed with a large number of granular noise.It is difficult to conduct analysis and interpretation of the mine video images.In order to improve the quality of the mine video images,the histogram matching algorithm in spatial domain is introduced into wavelet transform domain,a image enhancement method based on the histogram matching method in wavelet transform domain is proposed.Firstly,the mine image and the mine video image processed by histogram matching method are conducted wavelet transform respectively,based on the gray histogram of the wavelet low-frequency sub-band processed by the histogram matching method,the wavelet low-frequency sub-band of the mine video image is matched with it,the enhanced wavelet low-frequency sub-band is acquired;then,based on analyzing the basic principle of the average filtering method,according to the deficiencies of the average filtering method,the weighted improved improved average filtering method based on adaptive division of filtering windows is put forward,and it is applied to filtered the granular noise distributed in the wavelet high-frequency sub-bands of the mine underground video image;finally,the wavelet low-frequency sub-band processed histogram matching method and the wavelet high-frequency sub-bands filtered by the improved average filtering method proposed in this paper are conducted inverse wavelet transform,the mine video image with perfect visual effects is obtained.Programs of the histogram equalization,homomorphic filtering,average filtering and the method proposed in this paper are obtained by C++ language respectively.The experimental data is the underground video image obtained in a mine of Yangquan city,Shanxi province,the edge protection index (EPI) is used to evaluate the effects of the above methods,the results show that,the performance of the method proposed in this paper is superior to the other methods.

Key words: Mine video monitoring system,Image enhancement,Wavelet transform,Histogram equalization, Histogram matching, Average filtering, Edge protection index