Open AccessImage fusion via feature residual and statistical matching
Author(s) -
Wang Lijuan,
Han Jing,
Zhang Yi,
Bai Lianfa
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0280
Subject(s) - image fusion , residual , artificial intelligence , pattern recognition (psychology) , discrete wavelet transform , feature (linguistics) , fusion , matching (statistics) , computer science , computer vision , feature extraction , image (mathematics) , mathematics , wavelet transform , wavelet , algorithm , statistics , linguistics , philosophy
In view of the shortcoming of traditional image fusion based on discrete wavelet transform (DWT) with unclear textural information, an effective visible light and infrared image fusion algorithm via feature residual and statistical matching is proposed in this study. First, the source images are decomposed into low‐frequency coefficients and high‐frequency coefficients by DWT. Second, two different fusion schemes are designed for the low‐frequency coefficients and high frequency ones, respectively. The low‐frequency coefficients are fused by a local feature residual‐based scheme to achieve adaptive fusion; the high‐frequency coefficients are accomplished by a local statistical matching‐based scheme to extract the edge information effectively. Finally, the fused image is obtained by inverse DWT. Experimental results demonstrate that the proposed method can produce a more accurate fused image, leading to an improved performance compared with existing methods.