Super-resolution Sampling

Super-Resolution Sampling is a computational image sampling technique that enhances spatial resolution by reconstructing high-resolution details from low-resolution inputs through learned or optimized sampling patterns.

16

Publications

568

Citations

81

Authors

41

Institutions

Publications per year

2017–2026

6

Authors

81

  1. QZ
    Qi Zhao

    Shenzhen Polytechnic University, China

    1 publications · 34 citations

    1

    h-index

  2. TN
    Takayuki Nishizaka

    Gakushuin University, Japan

    1 publications · 55 citations

    1

    h-index

  3. GC
    Guanlin Chen

    Zhejiang University, China

    1 publications · 18 citations

    1

    h-index

  4. SG
    Srinivasa G. Narasimhan

    Carnegie Mellon University, United States

    1 publications · 65 citations

    1

    h-index

  5. WE
    W. E. Moerner

    Stanford University, United States

    1 publications · 131 citations

    1

    h-index

1–5 of 81

Venues

  1. IEEE Transactions on Signal Processing

    2 publications · 34 citations

    2

    h-index

  2. 1

    h-index

  3. Applied Optics

    1 publications · 16 citations

    1

    h-index

  4. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 42 citations

    1

    h-index

  5. ACS Nano

    1 publications · 55 citations

    1

    h-index

1–5