Abstract
Due to the limitations of imaging sensors, remote sensing images often have limited resolution. To address this issue, various super-resolution (SR) image reconstruction techniques have been developed to reconstruct a high-resolution image from a sequence of low-resolution, noisy and blurry observations. In this paper, we propose an efficient super-resolution image reconstruction method for geometrically deformed remote sensing images, based on the nonlocal total variation (NLTV) regularization. The proposed minimization problem is solved by a fast primal-dual algorithm. Numerical experiments demonstrate the performance of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings |
| Pages | 8050-8053 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538671504 |
| DOIs | |
| State | Published - Oct 31 2018 |
| Event | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: Jul 22 2018 → Jul 27 2018 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| Volume | 2018-July |
Conference
| Conference | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 |
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| Country/Territory | Spain |
| City | Valencia |
| Period | 7/22/18 → 7/27/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE
Funding
The authors would like to thank Stamatis Lefkimmiatis from Skolkovo Institute of Science and Technology for providing advice on efficiently implementing the NLTV regularization.
| Funders | Funder number |
|---|---|
| Akademiet for de Tekniske Videnskaber |
Keywords
- Primal-dual algorithm
- Remote sensing images
- Super-resolution image reconstruction
ASJC Scopus subject areas
- Computer Science Applications
- General Earth and Planetary Sciences