Mine image enhancement algorithm based on multi-scale fast bilateral filtering and wavelet transform
WANG Yuanbin
HE Dongyang
FAN Hongwei
WANG Xu
HE Wenqing
YAN Zhaoxu
LI Qianxi
Abstract：Due to complex geological conditions and unevenly artificial lighting in underground coal mines,surveillance video images of-ten exhibit non-uniform illumination,detail loss,and low contrast.Moreover,existing enhancement algorithms frequently introduce color distortion and halo artifacts during processing.To address these issues,a mine image enhancement algorithm based on multi-scale fast bi-lateral filtering and wavelet fusion is proposed.First,homomorphic filtering is applied to preliminarily enhance the image and convert it into HSV space,where the hue component remains unchanged.A multi-scale fast bilateral filter is then constructed to extract the illumina-tion component from the brightness channel,and a dual gamma correction function is employed to enhance the illumination component.The reflection component,estimated according to Retinex theory,is further enhanced using a grayscale adjustment function and the Con-strained Contrast Adaptive Histogram Equalization(CLAHE)algorithm.Illumination and reflection components are subsequently fused by wavelet transform to obtain the enhanced brightness channel.In addition,a saturation correction function is designed to improve the satur-ation component and enhance the overall color representation of the mine image.Finally,the enhanced brightness and saturation compon-ents are combined with the hue component and transformed back from HSV to RGB space.Experimental results demonstrate that,com-pared with BPDHE,CLAHE,NPE,SRIE,BIMEF,and PnPRetinex algorithms,the proposed method achieves respective improvements of 25.31%,42.75%,9.59%,1.60%,and 41.26%in objective evaluation metrics including mean,average gradient,standard deviation,inform-ation entropy,and spatial frequency.The method effectively enhances illumination,details,and contrast of mine images while suppressing halo artifacts and color distortion.Moreover,when extracting illumination components,multi-scale fast bilateral filtering achieves an aver-age speed improvement of 87.29%compared with the classical bilateral filter.When YOLOv8 is applied to the enhanced images of mine workers,an average detection accuracy of 90%is obtained,representing a 40%increase compared with the original images and signific-antly improving the accuracy of intelligent detection.
Keywords：Retinex theoryfast bilateral filteringHSV spacedual gamma correction functionwavelet transform
Publication Date：2025-10-31
Online Publishing Date：2025-11-18(First online date of this platform, not the publication date of the document)
Pages：14( 237-250 )