Abstract: In order to solve the problem of image degradation in video monitoring systems caused by complex lighting conditions in mines,a fuzzy enhancement algorithm for coal mine degradation images based on adaptive wavelet transform is proposed.Firstly,the degraded image is decomposed into low-frequency sub-graph and high-frequency sub-graphs of different scales by multi-scale wavelet decomposition,and the wavelet shrinkage threshold method of Bayesian estimation is used to adaptively adjust the wavelet threshold at different scales.Secondly,an adaptive wavelet threshold function that introduces adaptive weight factor and adaptive enhancement coefficient is designed,which not only maintains the continuity of the threshold function and avoids fixed deviations,but also implements contraction threshold filtering and non-linear en-hancement for high-frequency sub-graphs of different scales.Thirdly,a bilateral filtering algorithm is used to estimate and remove the illuminance component in the low-frequency sub-graph,and the wavelet reconstruction is performed on the processed low-frequency sub-graph and high-frequency sub-graphs of each scale to obtain enhanced wavelet reconstruction images.Finally,the improved membership function and fuzzy enhancement operator are used to adjust the brightness component of wavelet reconstructed image to obtain the final enhanced image.The subjective vision and objective evaluation indicators are used to analyze the results of degraded image enhancement experiments.The proposed algorithm has the best image enhancement effect,which can effectively suppress image noise,enhance detailed information,reduce image distortion,improve the visual effect of degraded images,overcome the limitations of traditional image enhancement algorithms under complex lighting conditions in mines,and has strong robustness.Compared with CLAHE,SSR,MSR,BF-DCP,DGR,MSWT and PGCHE,its comprehensive performance indicators have been improved by 4.42%,4.95%,15.35%,196.60%,88.93%,10.52% and 12.10% respectively.