煤矿井下图像的双分支耦合Transformer网络增强方法
TD76%TP391.41; 受煤矿井下光源少、光照不均匀等因素影响,井下采集到的图像存在照度低、呈现大量暗区、细节信息模糊或缺失、过暗产生噪声等问题.现有图像增强方法在进行低照度图像增强时容易出现色彩失真、细节信息丢失等缺点,另外深度学习低照度图像增强方法在一定程度上解决了低照度图像亮度增强的问题,但其模型泛化能力较差,在实际煤矿井下场景应用效果不佳.针对上述问题,利用Transformer本身泛化能力强的优势,提出一种基于Transformer模型的低照度图像增强算法.融合Swin v2模块与卷积模块,构建煤矿井下低照度图像的乘法图和加法图,并与原图像进行叠加拟合,以解决细节信息模糊或缺失...
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| Published in | 煤炭学报 Vol. 49; no. 9; pp. 4027 - 4037 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
煤炭科学研究总院有限公司矿山大数据研究院,北京 100013
01.09.2024
煤炭智能开采与岩层控制全国重点实验室,北京 100013%煤炭科学研究总院,北京 100013 煤炭智能开采与岩层控制全国重点实验室,北京 100013%煤炭科学研究总院有限公司矿山大数据研究院,北京 100013 煤炭科学研究总院,北京 100013 天地科技股份有限公司,北京 100013 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-9993 |
| DOI | 10.13225/j.cnki.jccs.2023.0997 |
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| Abstract | TD76%TP391.41; 受煤矿井下光源少、光照不均匀等因素影响,井下采集到的图像存在照度低、呈现大量暗区、细节信息模糊或缺失、过暗产生噪声等问题.现有图像增强方法在进行低照度图像增强时容易出现色彩失真、细节信息丢失等缺点,另外深度学习低照度图像增强方法在一定程度上解决了低照度图像亮度增强的问题,但其模型泛化能力较差,在实际煤矿井下场景应用效果不佳.针对上述问题,利用Transformer本身泛化能力强的优势,提出一种基于Transformer模型的低照度图像增强算法.融合Swin v2模块与卷积模块,构建煤矿井下低照度图像的乘法图和加法图,并与原图像进行叠加拟合,以解决细节信息模糊或缺失、过暗产生噪声的问题.同时采用多尺度模块的注意力机制对叠加拟合后的图像进行色彩处理,以解决图像亮度提升有限、存在大量暗区以及色彩失真的问题.经实验表明,相较于LIME、Zero-DCE、RetiNexNet、MBLLEN、KIND算法,本文算法在客观质量指标峰值信噪比(PSNR)和结构相似性(SSIM)上的表现,分别提高了34.76%、55.73%,47.32%、52.76%,22.52%、25.7%,19.615%、12.285%,5.81%、2.625%.同时定性分析结果表明该方法能够对煤矿井下低照度图像进行显著增强,图像亮度达到可视范围,相比其他方法,色彩更加真实,图像细节信息更为清晰.说明本文提出的算法在图像噪声程度、色彩失真程度、对比度、结构相似度以及亮度等方面均具有良好的性能,整体相对较优. |
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| AbstractList | TD76%TP391.41; 受煤矿井下光源少、光照不均匀等因素影响,井下采集到的图像存在照度低、呈现大量暗区、细节信息模糊或缺失、过暗产生噪声等问题.现有图像增强方法在进行低照度图像增强时容易出现色彩失真、细节信息丢失等缺点,另外深度学习低照度图像增强方法在一定程度上解决了低照度图像亮度增强的问题,但其模型泛化能力较差,在实际煤矿井下场景应用效果不佳.针对上述问题,利用Transformer本身泛化能力强的优势,提出一种基于Transformer模型的低照度图像增强算法.融合Swin v2模块与卷积模块,构建煤矿井下低照度图像的乘法图和加法图,并与原图像进行叠加拟合,以解决细节信息模糊或缺失、过暗产生噪声的问题.同时采用多尺度模块的注意力机制对叠加拟合后的图像进行色彩处理,以解决图像亮度提升有限、存在大量暗区以及色彩失真的问题.经实验表明,相较于LIME、Zero-DCE、RetiNexNet、MBLLEN、KIND算法,本文算法在客观质量指标峰值信噪比(PSNR)和结构相似性(SSIM)上的表现,分别提高了34.76%、55.73%,47.32%、52.76%,22.52%、25.7%,19.615%、12.285%,5.81%、2.625%.同时定性分析结果表明该方法能够对煤矿井下低照度图像进行显著增强,图像亮度达到可视范围,相比其他方法,色彩更加真实,图像细节信息更为清晰.说明本文提出的算法在图像噪声程度、色彩失真程度、对比度、结构相似度以及亮度等方面均具有良好的性能,整体相对较优. |
| Abstract_FL | Affected by factors such as few light sources and uneven illumination in underground coalmines,underground images have some problems such as low illumination,presenting many dark areas,blurring or missing detail information,excessive darkness generating noise,etc.Traditional image enhancement methods are prone to some shortcomings such as color distortion and loss of detail information in low-light image enhancement.Furthermore,a deep-learning low-light im-age enhancement method can solve the problem of low-light image brightness enhancement to a certain extent,but its model generalization ability is poor in real-world scenarios.Aiming at above mentioned problems,taking the advantage of Transformer's strong generalization ability,a low illumination image enhancement algorithm based on Transformer model is proposed.Firstly,the Swin v2 module is combined with the convolution module to construct the multiplicative and ad-ditive maps of the underground low illuminance image,and superimposed with the original image for fitting,in order to solve the problems of blurring or missing detail information,and over-darkness generating noise.At the same time,the at-tention mechanism of the fusion multi-scale module is used to perform color processing on the superimposed fitted image to solve the problems of limited image brightness enhancement,the existence of many dark areas,and color distortion.The experiments verify that the performances of this paper's algorithm on the objective quality metrics Peak Signal to Noise Ratio(PSNR),Structural Similarity(SSIM),are improved by 34.76%,55.73%;47.32%,52.76%;22.52%,25.7%;19.615%,12.285%;5.81%,2.625%,compared to LIME,Zero-DCE,RetiNexNet,MBLLEN,and KIND algorithms.Meanwhile,the qualitative analysis results show that the proposed method can significantly enhance the low illumination image of the underground coalmine,the image brightness reaches the visible range,the color is more realistic compared to other methods,and the image detail information is clearer.The study shows that the proposed algorithm has a good per-formance in terms of the degree of image noise,color distortion,contrast,structural similarity,and brightness,which is re-latively superior overall. |
| Author | 李昊 宋泽龙 马永壮 李和平 程健 孙大智 |
| AuthorAffiliation | 煤炭科学研究总院,北京 100013;煤炭科学研究总院有限公司矿山大数据研究院,北京 100013;天地科技股份有限公司,北京 100013;煤炭智能开采与岩层控制全国重点实验室,北京 100013%煤炭科学研究总院,北京 100013;煤炭科学研究总院有限公司矿山大数据研究院,北京 100013;煤炭智能开采与岩层控制全国重点实验室,北京 100013%煤炭科学研究总院有限公司矿山大数据研究院,北京 100013;天地科技股份有限公司,北京 100013 |
| AuthorAffiliation_xml | – name: 煤炭科学研究总院,北京 100013;煤炭科学研究总院有限公司矿山大数据研究院,北京 100013;天地科技股份有限公司,北京 100013;煤炭智能开采与岩层控制全国重点实验室,北京 100013%煤炭科学研究总院,北京 100013;煤炭科学研究总院有限公司矿山大数据研究院,北京 100013;煤炭智能开采与岩层控制全国重点实验室,北京 100013%煤炭科学研究总院有限公司矿山大数据研究院,北京 100013;天地科技股份有限公司,北京 100013 |
| Author_FL | SONG Zelong SUN Dazhi LI Heping CHENG Jian LI Hao MA Yongzhuang |
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| DOI | 10.13225/j.cnki.jccs.2023.0997 |
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| DocumentTitle_FL | A novel image enhancement method via dual-branch coupled Transformer network for underground coalmine |
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| Keywords | Transformer model image enhancement 煤矿井下 low-illumination images 低照度图像 图像增强 Transformer模型 underground coalmine |
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| PublicationTitle_FL | Journal of China Coal Society |
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| Title | 煤矿井下图像的双分支耦合Transformer网络增强方法 |
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