Low-illumination mine image enhancement algorithm based on improved EnlightenGAN
TIAN Feng
WANG Mengjiao
LIU Xiaopei
WEI Xijie
ZHAO Weiping
Abstract：Video surveillance is one of the important means of coal mine safety monitoring,and the poor quality of video surveillance im-ages affects the accuracy of the video surveillance system due to the influence of illumination,dust and water mist,so it is of great signific-ance to study the mine low-illumination image enhancement algorithm.To address the problems of texture feature loss and colour distor-tion during the enhancement of extremely dark low-illumination images in coal mines,an unsupervised mine low-illumination image en-hancement algorithm EMGAN based on the improved EnlightenGAN is proposed.Firstly,in the generator network,the original U-Net model is replaced by the ResU-Net model,and the residual linking mechanism is added to enhance the feature transmission ability,effect-ively alleviate the gradient disappearance problem in training,retain the detail information in the image,and introduce an efficient multi-scale attention module for cross-space learning in the down-sampling phase of the network to improve the feature extraction ability of the network in complex environments;secondly,construct a dual discriminator network based on the PatchGAN,which discriminates the global and local regions of the image respectively,and efficiently balances the overall image brightness and contrast;finally,the joint loss function is designed to combine the perceptual loss and colour consistency loss to avoid colour distortion.Based on the self-constructed coal mine underground dataset for validation,the mean,standard deviation,information entropy,average gradient,peak signal-to-noise ra-tio,and structural similarity of the improved algorithm are improved by an average of 6.84%,24.88%,7.54%,8.30%,27.40%,and 10.85%,respectively.The experimental results show that the algorithm improves the brightness and contrast of the extremely dark and low-illumination image of the mine while retaining the texture feature information of the image and avoiding the colour distortion phenomenon,which effectively improves the quality of the image and provides a more reliable basis for the subsequent video monitoring and analysis.
Keywords：image enhancementlow-illuminationEnlightenGANResU-Netmulti-scale attention
Publication Date：2025-11-30
Online Publishing Date：2025-12-15(First online date of this platform, not the publication date of the document)
Pages：11( 117-127 )