Abstract: Due to the complexity of imaging environment of mine，the acquired mine video image is relatively vague and blended with a amount of noise，so，it is necessary to take the appropriate method to filter out the noise and improve the resolution of the image.A new wavelet domain mine video image denoising algorithm based on edge detection is proposed so as to deal with the noise image.Firstly，the noise image is pre-processed by adopting average filtering algorithm；secondly，the classical Prewitt operator is improved from the perspectives of detection templates and adaptive threshold setting method，and the improved Prewitt operator is adopted to extract the edge profile information of pre-processed image to obtain the edge image and the non-edge image;finally，the non-edge image is filtered by the improved wavelet threshold function model based on adaptive threshold，the new function model is a combination of the advantage of wavelet hard and wavelet soft threshold function model and the threshold of the new function model can be changed adaptively according to the layer number of wavelet decomposition.In order to verify the effectiveness of the proposed algorithm，the two mine video surveillance monitoring system images obtained in a mine of Yanzhou，Shandong province are taken as the experiment data，the experimental results show that，the performance of the algorithm in this paper is better than the wavelet hard function model，wavelet soft function model and the two existed improved wavelet function model.Besides that，the time-consuming of the algorithm proposed is shorter than the two existing improved wavelet function model，and it also has some reference for enhance the quality of the mine video surveillance monitoring system images.

Key words: Digital mine, Mine video image, Edge detection, Prewitt operator, Wavelet transform, Wavelet threshold function model