Open AccessMedical Image Fusion Based on Sparse Representation and Guided Filtering
Author(s) -
Sa Huang,
Guangyu Chu,
Yifan Fei,
Xiaoli Zhang,
Hailiang Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022045
Subject(s) - sparse approximation , image fusion , image (mathematics) , computer science , artificial intelligence , fuse (electrical) , representation (politics) , fusion , pattern recognition (psychology) , discrete cosine transform , computer vision , composite image filter , algorithm , engineering , linguistics , philosophy , politics , law , political science , electrical engineering
In this paper, we proposed a medical image fusion algorithm based on sparse representation and guided filtering. One of attractive features in the algorithm is that it can preserve the structural information of structural image and color information of functional image. We use a sparse representation in low frequency, initialize the dictionary by using DCT transform, and train the dictionary with each input source image as a training example. It not only ensures time complexity, but also ensures that the low-frequency fusion rules are adaptive. At high frequencies, we use the method of guided filtering to extract structural information from high-frequency images, and use the injection method to fuse high-frequency sub-bands to ensure the validity and richness of structural information. Experimental results show that the proposed fusion algorithm is superior to comparative algorithms in terms of subjective and objective evaluation methods.