## 1 Introduction  
Coal mine video monitoring systems are the primary means of monitoring coal mine safety production, and the timely and accurate identification of abnormal behaviour in the video is of great importance in coal mine safety production. Due to the poor lighting conditions and dusty underground production, lack of clarity in underground monitoring videos is an important issue at present, and the process of enhancing subsurface images can easily cause halo expansion and image overexposure. Therefore, eliminating halo artefacts is essential for image enhancement and further analysis. Traditional image-denoising methods are generally based on spatial-domain- and transform-domain-based image denoising. The Retinex model, based on Retinex theory, also has many improved versions, including the single-layer Retinex model [1], a multi-scale Retinex model [2], fusion-based enhancement methods [3], illumination map estimation [4], etc. There are also algorithms based on the variational Retinex model, such as the variational Retinex model as a quadratic optimisation problem [5], the Retinex variational framework for the introduction of bright channels [6], the variational Retinex model based on the L2 paradigm [7], a hybrid L2-LP variational model with a brilliant track prior [8], and the maximum entropy-based Retinex model. Based on the computational complexity of the variational approach, the disadvantage of the method is that it is time consuming to process the images. In addition, [PERSON] et al. [9] proposed a new edge-preserving filtering Retinex algorithm that embeds gradient-domain-guided image filtering (GGF), prior information on reflections and illumination into a Retinex-based variational framework to improve image structure and reduce artefacts or noise. [PERSON] and [PERSON] [10] proposed an adaptive image mapping method based on the Retinex model with a parametric non-linear mapping function. Only one luminance channel is used to estimate the reflectance component of the observed shimmering image. [PERSON] [11] proposed a mining image enhancement method based on the cuckoo search (CS) algorithm, which is based on the HSV colour space and uses the cuckoo search (CS) algorithm in combination with a proposed new conversion function that exploits the advantages of the bilateral gamma adjustment (BIGA) function and the dual platform histogram equalisation (DPHE) function. By finding the optimal parameter values, the image contrast and brightness are globally enhanced, and the details of the images are enhanced. In addition, [PERSON] [12] proposed a hybrid algorithm (SSR-BF) based on the combination of single-scale Retinex (SSR) and bilateral filtering (BF) to improve the image quality of surveillance videos. Combining BF with SSR reduces noise and improves edge information in the image. The schematic diagram and pseudo-code of SSR-BF are designed, and through simulation, the parameters are set reasonably to ensure the enhancement effect. [PERSON] et al. [13] proposed a hierarchical feature mining network (HFMNET), which extracts illumination and edge features in different network layers, constructs a feature mining attention (FMA) module, and combines hierarchical supervised loss to mine key features in the appropriate network layers. [PERSON] [14] proposed an enhancement algorithm based on an optimised homomorphic filter and RGB colour correction and developed an optimised homomorphic filter to reduce the interference of impurities on the image. The interference of impurity particles on the picture is reduced, and a preliminary optimised image can be obtained. A multi-scale image enhancement algorithm is designed to enhance the image with colour, and an RGB correction algorithm is proposed to colour correct the vision to further improve the image quality considering the colour deviation of the image. [PERSON] [15] proposed a multi-scale fusion framework for image enhancement combining Retinex and transmittance optimisation.