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潜在低秩表示框架下基于卷积神经网络结合引导滤波的红外与可见光图像融合 被引量:6

Infrared and Visible Image Fusion in Latent Low Rank Representation Framework Based on Convolution Neural Network and Guided Filtering
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摘要 为提高融合图像的可视性,解决传统红外与可见光图像融合算法中存在的边缘特征缺失、细节模糊的问题,提出了一种潜在低秩表示框架下基于卷积神经网络结合引导滤波的红外与可见光图像融合算法。该算法首先利用潜在低秩表示对源图像进行分解,得到源图像的低秩分量和显著分量。其次,利用卷积神经网络根据源图像的特征信息,得到权值图。再次,通过引导滤波算法对权值图进行边缘锐化,然后再将优化后的权值图分别与源图像的低秩分量和显著分量融合,得到融合图像的低秩分量和显著分量。最后,将融合图像的低秩分量和显著分量叠加,得到最终的融合图像。实验结果表明,该算法在主观评价和客观指标上均优于传统的红外与可见光图像融合算法。 In order to improve the visibility of fused images and solve the problems of missing edge features and fuzzy details in traditional infrared and visible image fusion algorithms,an novel image fusion algorithm in latent low rank representation framework based on convolution neural network and guided filtering was proposed.Firstly,the source images are decomposed to low-rank parts and saliency parts by latend lowrank representation.Secondly,according to the pixel activity information of source images,the weight maps are obtained through the convolution neural network.Thirdly,the weight maps are improved by guided filtering according to the source images and through that the weight maps of low-rank parts and saliency parts can be obtained respectively.Then,the weight maps are fused with low-rank parts and saliency parts of original images to obtain the low-rank part and the saliency part of fused image.Finally,the final fused image can be obtained by adding the fused low-rank part and the fused significant part.Compared with other fusion algorithms,the experimental result shows that the proposed algorithm is superior to the traditional infrared and visible image fusion algorithms in terms of subjective visual effects and objective indexes.
作者 娄熙承 冯鑫 LOU Xicheng;FENG Xin(Key Laboratory of Manufacturing Equipment Mechanism Design and Control of Chongqing,College of Mechanical Engineering,Chongqing Technology and Business University,Chongqing 400067,China)
出处 《光子学报》 EI CAS CSCD 北大核心 2021年第3期180-193,共14页 Acta Photonica Sinica
基金 国家自然科学基金(Nos.31501229,61861025) 重庆市基础研究与前沿探索项目(No.cstc2018jcyjAX0483) 重庆市教育委员会科学技术研究项目(Nos.KJQN201900821,KJQN202000803)。
关键词 红外与可见光图像 图像融合 潜在低秩表示 卷积神经网络 引导滤波 Infrared and visible image Image fusion Latent low-rank representation decomposition convolutional neural networks Guided filtering
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