## 1 Introduction  
With the rapid improvement of coal mine information [1; 2; 3], underground coal mine image enhancement has drawn considerable attention in image processing. However, due to the complicated underground environment and poor lighting conditions, coal mine image enhancement is a challenging problem. Usually caused by the presence of a large amount of suspended dust, the images obtained in coal mines are not clear and prone to the halo phenomenon [4; 5]. This kind of image makes it difficult to identify the region of interest, which reduces the quality of the image and the visual effect of human eyes [6; 7]. Therefore, it is necessary to enhance the image by image enhancement technology to highlight the detailed features of the original image and make the original blurred and low recognition image clear [8; 9; 10]. Image enhancement is essentially a noise reduction process, which enriches the favorable information in the image and eliminates or suppresses the worthless parts. At present, with the continuous innovation of image enhancement technology, many image enhancement algorithms have emerged [11; 12; 13], including gray transformation enhancement algorithms, frequency transformation enhancement algorithms and enhancement algorithms based on Retinex. However, the current image enhancement algorithms mainly focus on improving the brightness and contrast of uneven illumination images [14; 15; 16], while the problems of magnifying noise and losing weak texture detail information of mine images still exist. The image enhancement methods based on gray transformation mainly improve the brightness and contrast of the image through histogram equalization. [PERSON] et al. [17] optimized global histogram normalization and local histogram normalization through global and local constraints, which greatly improves the contrast of the original image. Basedon the dark primary color prior and the CLAHE algorithm, a novel image enhancement method was presented by [PERSON] et al. [18] to get clear images in the foggy coal mine. Aiming to solve the problem of reading errors caused by color deviation under abnormal light sources, [PERSON] et al. [19] proposed an adaptive color barcode image restoration algorithm. Considering that medical images acquired at insufficient lighting conditions suffer from low contrast issues, [PERSON] et al. [20] proposed a fuzzy and spline-based histogram equalization to perform contrast enhancement with medical images. In order to improve the effect of image acquisition under the condition of low illumination, [PERSON] et al. [21] adopted a low-illumination image enhancement algorithm based on multi-feature fusion. However, it can be seen from these studies that most algorithms focus only on the overall contrast of the image and have limited ability to suppress noise. Frequency domain image enhancement methods, such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT), decompose the image into subbands of different frequencies or directions and then process each subband separately. [PERSON] et al. [22] found that the fusion of multiple methods can effectively improve the effect of image enhancement and developed an image enhancement method based on DCT and DWT. Aiming to alleviate the image distortion caused by refraction and absorption of light depending on depth and color of water, [PERSON] et al. [23] proposed an underwater image enhancement algorithm based on DCT, DWT and FFT. The basic content of Retinex theory is that the color of an object is determined by its ability to reflect light waves rather than by the intensity of the reflected light. [PERSON] et al. [24] proposed a Retinex algorithm based on a guided filter, aiming to solve the problem that nighttime images have many dark spaces and shadows with low resolution. [PERSON] et al. [25] combined principal component analysis with a multi-scale retinex-based adaptive filter to enhance low-light-level images.