Feature-based Exposure Fusion

Feature-Based Exposure Fusion is a computational imaging technique in high-dynamic-range imaging that combines multiple differently exposed images by leveraging distinctive visual features—such as edges, textures, and salient structures—to guide the fusion process, thereby preserving optimal exposure in salient regions while enhancing overall image quality.

7

Publications

204

Citations

22

Authors

13

Institutions

Publications per year

2017–2026

5

Authors

22

  1. YS
    Yu Sun

    University of Toronto, Canada

    1 publications · 35 citations

    1

    h-index

  2. SZ
    Shaomin Zhang

    Zhejiang University, China

    1 publications · 32 citations

    1

    h-index

  3. ND
    Nai Ding

    Zhejiang University, China

    1 publications · 32 citations

    1

    h-index

  4. CW
    Chunmeng Wang

    Jinling Institute of Technology, China

    1 publications · 17 citations

    1

    h-index

  5. JL
    Jinyuan Liu

    Dalian University of Technology, China

    1 publications · 32 citations

    1

    h-index

1–5 of 22

Venues

  1. The Visual Computer

    2 publications · 35 citations

    2

    h-index

  2. Neurocomputing

    1 publications · 41 citations

    1

    h-index

  3. Information Fusion

    1 publications · 32 citations

    1

    h-index

  4. Optik

    1 publications · 18 citations

    1

    h-index

  5. 1

    h-index

1–5