Sparse Representation-based Image Fusion
Sparse Representation-Based Image Fusion is an image fusion framework that represents source images using sparse signal models, distinguishing itself by leveraging overcomplete dictionaries and sparsity-promoting optimization to preserve salient features in the fused output.
190
Publications
14.5K
Citations
587
Authors
211
Institutions
Publications per year
2017–2026
125
Parents
190
Image Fusion With Convolutional Sparse Representation
Yü Liu, Xun Chen, Rabab Ward et al. · IEEE Signal Processing Letters · 2016 · 977 citations
1–5 of 190
587
Chongqing University of Posts and Telecommunications, China
10 publications · 917 citations
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h-index
8
h-index
8
h-index
8
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7
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1–5 of 587
211

Xidian University
Xi'an, China
12 h-index
33 publications
1.3K citations

Chongqing University
Chongqing, China
11 h-index
36 publications
3.9K citations

Beihang University
Beijing, China
10 h-index
23 publications
1.6K citations
Chongqing University of Posts and Telecommunications
Chongqing, China
9 h-index
25 publications
815 citations

Northwestern Polytechnical University
Xi'an, China
9 h-index
27 publications
1.5K citations
Jiangxi University of Finance and Economics
Nanchang, China
8 h-index
38 publications
1.2K citations

Chinese Academy of Sciences
Beijing, China
7 h-index
7 publications
2.1K citations

Hunan University
Changsha, China
6 h-index
12 publications
3K citations
1–8 of 211
10 publications · 512 citations
10
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9 publications · 226 citations
9
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8 publications · 701 citations
8
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8 publications · 619 citations
8
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7 publications · 2.3K citations
7
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1–5