为提高煤矿井下低照度、大噪声图像的可观测性，提出了一种基于非下采样轮廓波变换的矿井图像增强算法，该方法克服了常规图像增强算法无法兼顾对比度提高与噪声抑制的不足。根据Retinex理论，推导出了低照度含噪声图像的Retinex增强框架，该框架解除了噪声对估计光照图的干扰，并且分离实现了图像的对比度提高和噪声抑制。依据该图像增强框架，首先利用非下采样轮廓波变换将输入图像分解为低频子带系数和高频方向子带系数，解除估计光照图与抑制噪声的耦合;然后在轮廓波变换域，利用R，G，B三个颜色通道的低频子带系数，求出3个低频子带系数的亮通道图像，但该亮通道图像存在细节突变和过低灰度值，不符合光照图缓慢变化的特征，对亮通道图像做进一步的Gamma校正和均值滤波，获得灰度值提高了的平滑光照图估计值;接着在轮廓波变换域，根据阈值函数收缩高频方向子带系数实现噪声抑制;最后，为突显某一频带方向的细节信息和提高整体对比度，将收缩的高频方向子带系数乘以相应的增益完成特定细节加强，再利用细节加强的高频子带系数、低频子带系数和光照图估计值重构出整体对比度提高的增强图像。数值实验表明，该图像增强算法能够有效地实现矿井图像的对比度提高、噪声抑制和细节加强，且具有良好的稳定性和适应性，能够很好地满足矿井下图像增强的需求。

To improve the observability mine images with the low illumination and high noise,a mine image enhancement algorithm based on non-subsampled contourlet transform is proposed. The algorithm overcomes the shortcomings of conventional image enhancement algorithms that cannot take into account both contrast enhancement and noise suppression. In the paper,an image enhancement framework based on Retinex for low illumination image with noise is deduced. The framework removes the interference of noise to the estimated illumination images,and separates the contrast enhancement and noise suppression of images. According to the framework,firstly,the input image is decomposed into low-frequency sub-band coefficients and high frequency directional sub-band coefficients by using subsampled contourlet transform,thus the coupling between esti-mating illumination image and suppressing noise is removed. Secondly,in the contourlet transform domain,the bright channel image of R,G and B channel is calculated using low frequency sub-band coefficients of three channels. However,the characteristics of details mutation and too low gray values of the bright channel image do not accord with the characteristics of slow change of illumination image. Thus,through further Gamma correction and mean filtering of the bright channel image,the estimated value of the smoothed illumination map with improved gray value is obtained. After that,in the contourlet transform domain,the high frequency direction subband coefficients are shrunk according to the threshold function to achieve noise suppression. At last,to highlight the details of a frequency band direction,the direction sub-band coefficients multiplied by the corresponding gain are used to enhance the details. The mine image with noise suppression and detail enhancement is reconstructed by using low frequency sub-band coefficients and high frequency directional sub-band coefficients with detail enhancement. In order to further improve the contrast,the reconstructed image is divided by the estimated illumination image to obtain the final enhanced image. To highlight the details of a certain band direction and improve the overall contrast,the contracted high frequency direction sub-band co-efficients are multiplied by the corresponding gain to complete the specific detail enhancement. The enhanced high frequency sub-band coefficients,the low-frequency sub-band coefficients and the estimated illumination map are used to reconstruct an enhanced image with improved overall contrast. Numerical experiments show that the algorithm can effectively improve the contrast,suppress noise and highlight details. Moreover,the algorithm has a better stability and adaptability,and can well meet the needs of mine image enhancement.