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

Authors

587

  1. ZZ
    Zhiqin Zhu

    Chongqing University of Posts and Telecommunications, China

    10 publications · 917 citations

    10

    h-index

  2. YY
    Yong Yang

    Jiangxi University of Finance and Economics, China

    8 publications · 254 citations

    8

    h-index

  3. GQ
    Guanqiu Qi

    Chongqing University of Posts and Telecommunications, China

    8 publications · 673 citations

    8

    h-index

  4. SH
    Shuying Huang

    Jiangxi University of Finance and Economics, China

    8 publications · 254 citations

    8

    h-index

  5. YL
    Yü Liu

    Hefei University of Technology, China

    7 publications · 3.1K citations

    7

    h-index

1–5 of 587

Venues

  1. Biomedical Signal Processing and Control

    10 publications · 512 citations

    10

    h-index

  2. IEEE Access

    9 publications · 226 citations

    9

    h-index

  3. Infrared Physics & Technology

    8 publications · 701 citations

    8

    h-index

  4. 8

    h-index

  5. Information Fusion

    7 publications · 2.3K citations

    7

    h-index

1–5