基于低秩稀疏矩阵分解和离散余弦变换实现多聚焦图像融合的算法  

Algorithm for Multifocus Image Fusion Based on Low-Rank and Sparse Matrix Decomposition and Discrete Cosine Transform

作　　者：史艳琼 王昌文 卢荣胜[2] 查昭 朱广 Shi Yanqiong;Wang Changwen;Lu Rongsheng;Zha Zhao;Zhu Guang(School of Mechanical and Electrical Engineering,Anhui Jianzhu University,Hefei 230601,Anhui,China;School of Instrument Science and Opto-Electronics Engineering,Hefei University of Technology,Hefei 230009,Anhui,China)

机构地区：[1]安徽建筑大学机械与电气工程学院,安徽合肥230601 [2]合肥工业大学仪器科学与光电工程学院,安徽合肥230009

出　　处：《激光与光电子学进展》2024年第10期408-415,共8页Laser & Optoelectronics Progress

基　　金：安徽省科技重大专项(202203a05020022);安徽建筑大学校引进人才及博士启动基金项目(2019QDZ16);安徽省研究生教育质量工程项目(2022cxcysj147);安徽省研究生教育质量工程项目(2022cxcysj156)。

摘　　要：针对多聚焦图像融合过程中存在聚焦边缘模糊、伪影和块效应的问题,提出一种基于低秩稀疏矩阵分解(LRSMD)和离散余弦变换(DCT)实现多聚焦图像融合的算法。首先,利用LRSMD将源图像分解为低秩和稀疏矩阵两部分;然后,设计DCT方法检测低秩矩阵部分聚焦区域,构建初始焦点决策图,并利用重复一致性验证方法验证决策图,同时设计基于形态滤波的融合策略,得到稀疏矩阵部分融合结果;最后,采用加权重构方法对两部分进行融合。实验结果表明,相较于其他5种主流算法,所提算法在主观评价上具有高清晰度和全聚焦的优势,在客观评价上,边缘信息保持度、峰值信噪比、结构相似性及相关系数4个指标最高分别提高了62.3%、6.3%、2.2%及6.3%,证明所提算法有效提升了对源图像聚焦信息的提取能力,增强了聚焦边缘细节信息,同时对伪影和块效应的减少起到了重要作用。To resolve the problems of scattered focus-edge blurring,artifacts,and block effects during the multifocus image fusion,an algorithm based on low-rank and sparse matrix decomposition(LRSMD)and discrete cosine transform(DCT)is designed to achieve the multifocus image fusion.First,the source images were decomposed into low-rank and sparse matrices using LRSMD.Subsequently,the DCT-based method was designed for detecting the focus regions in the low-rank matrix part and obtaining the initial focus decision map.The decision map was verified using the repeated consistency verification method.Meanwhile,the fusion strategy based on morphological filtering was designed to obtain fusion results of the sparse matrix.Finally,the two parts were fused using the weighted reconstruction method.The experimental results show that the proposed algorithm has the advantages of high clarity and full focus in subjective evaluations.The best results for the four metrics,including edge information retention,peak signal-to-noise ratio,structural similarity,and correlation coefficient in objective evaluations,improved by 62.3%,6.3%,2.2%,and 6.3%,respectively,compared with the other five mainstream algorithms.These improvement results prove that the proposed algorithm effectively improves focused information extraction from source images and enhances the focused edge detail information.Furthermore,the algorithm is crucial for reducing the artifact and block effects.

关 键 词：图像处理 图像融合 低秩稀疏矩阵分解 离散余弦变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]