可见光与红外图像组K-SVD融合方法  

Visible and Infrared Image Fusion Based on Group K-SVD

作　　者：王志社[1] 姜晓林 武圆圆 王君尧 WANG Zhishe;JIANG Xiaolin;WU Yuanyuan;WANG Junyao(School of Applied Science,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学应用科学学院,山西太原030024

出　　处：《红外技术》2021年第5期455-463,共9页Infrared Technology

基　　金：山西省面上自然基金项目(201901D111260);信息探测与处理山西省重点实验室开放研究基金(ISTP2020-4);山西省“1331”工程重点创新团队建设计划资助(20193-3);太原科技大学博士启动基金(20162004)。

摘　　要：传统稀疏表示融合方法,以图像块进行字典训练和稀疏分解,由于没有考虑图像块之间的内在联系,易造成字典原子表征图像特征能力不足、稀疏系数不准确,导致图像融合效果不好。为此,本文提出可见光与红外图像组K-SVD(K-means singular value decomposition)融合方法,利用图像的非局部相似性,将相似图像块构造成图像结构组矩阵,通过组K-SVD进行字典训练和稀疏分解,可以有效提高字典原子的表征能力及稀疏系数的准确性。实验结果表明,该方法在主观和客观评价上都优于传统稀疏融合方法。In the traditional image fusion method based on sparse representation,image blocks are used as units for dictionary training and sparse decomposition.The representation ability of dictionary atoms for image features is insufficient if the internal connection between the image blocks is not considered.Moreover,the sparse coefficients are inaccurate.Therefore,a fused image is not desirable.In view of the abovementioned problem,this paper proposes a fusion method based on the group K-means singular value decomposition(K-SVD)for visible and infrared images.Considering the image non-local similarity,this method constructs a structure group matrix using similar image blocks,and then,dictionary training and sparse decomposition are performed in the units of the structure group matrix by group K-SVD.Thus,this method can effectively improve the representation ability of dictionary atoms and the accuracy of the sparse coefficients.The experimental results show that this method is superior to the traditional sparse fusion method in terms of subjective and objective evaluation.

关 键 词：图像融合 非局部相似性 结构组矩阵 组K-SVD 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]