基于深度神经网络的煤矿井下低光照图像增强算法  

Deep neural network-based image enhancement algorithm for low-illumination images underground coal mines

作　　者：王满利 张航 李佳悦 张长森 WANG Manli;ZHANG Hang;LI Jiayue;ZHANG Changsen(School of Physics&Electronic Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学物理与电子信息学院,河南焦作454000

出　　处：《煤炭科学技术》2023年第9期231-241,共11页Coal Science and Technology

基　　金：国家自然科学基金资助项目(52074305);河南省科技攻关资助项目(212102210005);山西省重点研发计划资助项目(2020XXX001)。

摘　　要：由于煤矿井下空间环境的复杂性与恶劣的光照条件,视觉设备获取的图像容易存在对比度不足、纹理细节差等问题,严重影响了视觉设备的工作可靠性,限制了进一步的基于图像的智能视觉应用。为提高矿井下低照度图像的对比度,同时强化其纹理细节,提出一种基于深度神经网络的矿井下低光照图像增强模型,该模型包含有3个子网络,分别为分解网络、光照调整网络和反射重构网络。分解网络将煤矿井下图像分解为光照分量和反射分量;光照调整网络利用深度可分离卷积结构有效减少了模型的参数,强化了网络特征提取能力,此外,引入了MobileNet网络结构,进一步使光照调整网络轻量化,同时保持其特征提取精度,有效实现光照分量对比度调整;反射重构网络引入了残差网络结构,提升了网络特征学习性能与反射分量纹理细节恢复能力;最后,将处理过后的光照分量和反射分量基于Retinex理论融合,获得增强图像,实现矿井下图像的对比度提高与细节的增强,克服了现有增强算法中所存在的增强图像细节丢失、边缘模糊、对比度和清晰度不足等问题。数值试验表明,提出的模型能够在提高矿井下图像对比度的同时有效强化图像的纹理细节,并且具有良好的稳定性和鲁棒性,能够很好地满足煤矿井下低光照图像增强的需求。Due to the complexity of the spatial environment and poor lighting conditions in underground coal mines,the images obtained by vision devices are prone to problems such as insufficient contrast and poor texture details,which seriously affect the reliability of the work of vision devices and limit further image-based intelligent applications.To improve the contrast of low-illumination images in underground mines while enhancing their texture details,a deep neural network-based low-illumination image enhancement model is proposed,which contains three sub-networks,namely,decomposition network,illumination adjustment network and reflection reconstruction network.The decomposition network decomposes the underground coal mine image into light and reflection components;the light adjustment network effectively reduces the parameters of the model using depth-separable convolutional structure and strengthens the feature extraction ability of the network;in addition,the MobileNet network structure is introduced to further lighten the light adjustment network while maintaining its feature extraction accuracy and effectively realizing the contrast adjustment of light components;the reflection reconstruction network introduces a residual network structure to improve the contrast adjustment of light components.Finally,the processed illumination and reflection components are fused based on Retinex theory to obtain enhanced images,which achieve contrast enhancement and detail enhancement of underground mine images,overcoming the problems of detail loss,blurred edges,and lack of contrast and clarity of the enhanced image that exist in existing enhancement algorithms.Numerical experiments show that the proposed mod-el can effectively enhance the texture details of the image while improving the contrast of underground mine images,and has good stabil-ity and robustness,which can well meet the needs of low-light image enhancement in coal mines.

关 键 词：图像增强 图像分解 RETINEX理论 亮度调整 卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TD67[自动化与计算机技术—计算机科学与技术]