Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization

By Renwei Dian, Leyuan Fang, Shutao Li. Computer Vision and Pattern Recognition, 2017

298 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/396bbd618cff7e38c585a7af3fc3517cf5fb3a94

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Leyuan Fang
2. Renwei Dian
3. Shutao Li
4. Anjing Guo
5. Yu Liu
6. Xun Chen
7. Zengfu Wang
8. Jiayi Ma

In Media Technology

1. Leyuan Fang
2. Renwei Dian
3. Shutao Li
4. Anjing Guo
5. Yu Liu
6. Xun Chen
7. Xudong Kang
8. Zengfu Wang

Citations in the corpus

• Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, methodology): “We compare the LTTR method with three current state-of-the-art HSI super-resolution approaches, including non-negative structured sparse representation (NSSR) [33], coupled spectral unmixing (CSU) [28], and nonlocal sparse tensor factorization method (NLSTF) [35].”
• Deep Hyperspectral Image Sharpening (2018, methodology): “Both of CAVE database and Harvard database are widely used in HSI sharpening literatures [1], [37], [39], [42], [54]–[56].”