基于低秩分解和卷积稀疏编码的多源图像融合  

Multi-Source Image Fusion Based on Low-Rank Decomposition and Convolutional Sparse Coding

作　　者：王加新 陈升 谢明鸿[1] Wang Jiaxin;Chen Sheng;Xie Minghong(Faculty of lnformatio Eingineering and Automation,Kunming University of Science and Technology,Kunming,Yunan,650500,China;Gree Electric Appliances,INC.of Zhuhai,Zhuhai,Guangdong 519000,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500 [2]珠海格力电器股份有限公司,广东珠海519000

出　　处：《激光与光电子学进展》2021年第22期173-181,共9页Laser & Optoelectronics Progress

摘　　要：针对卷积稀疏编码能够较好地保留图像信息特征的这一特点,提出基于低秩分解和卷积稀疏编码的多源图像融合方法。为了避免图像分块处理对图像结构的影响,将每幅待融合图像进行全局处理。首先,通过低秩分解将图像分解成低秩和稀疏两部分;接着,对稀疏部分进行卷积分解,可以训练得到一组稀疏滤波器字典,再将卷积稀疏编码应用到图像的融合中;然后,对低秩和稀疏成分分别设计不同的融合规则,得到融合低秩成分和融合稀疏成分,最终得到融合图像。最后,为了验证所提方法的融合效果,将所提方法与其他方法进行对比实验。实验结果表明,所提方法在视觉效果和客观评价指标方面均取得良好的效果。Aiming at the feature that convolutional sparse coding can better retain image information features,a multi-source image fusion method based on low-rank decomposition and convolutional sparse coding is proposed.In order to avoid the impact of image block processing on the image structure,each image to be fused is processed globally.First,the image is decomposed into low-rank and sparse parts by low-rank decomposition.Then,a set of sparse filter dictionaries can be trained by convolution decomposition of sparse parts,and the convolution sparse coding is applied to image fusion.Second,different fusion rules are designed for the low-rank and sparse components to obtain the low-rank and sparse components,and finally the fusion image is obtained.Finally,in order to verify the fusion effect of the proposed method,the proposed method is compared with other methods.The experimental results show that the proposed method has achieved good results in terms of visual effects and objective evaluation indicators.

关 键 词：图像处理 图像融合 低秩分解 稀疏表示 卷积稀疏编码 

分 类 号：O436[机械工程—光学工程]