TY - GEN
T1 - An effective super-resolution reconstruction method for geometrically deformed image sequences
AU - Qin, Jing
AU - Yanovsky, Igor
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Despite of the technology advancements, remote sensing images usually suffer from a poor spatial resolution. To resolve this issue, a lot of research efforts have been devoted to developing resolution enhancement methods which retrieve a high-resolution image out of its low-resolution degraded versions. In this paper, we consider a nonlocal total variation (NLTV) based super-resolution method which handles low-resolution images with geometric deformations. In particular, we apply the framework of alternating direction method of multipliers (ADMM) to deduce an effective algorithm, which involves soft thresholding and gradient descent. Effectiveness and robustness to noise of the proposed method are verified by various numerical experiments.
AB - Despite of the technology advancements, remote sensing images usually suffer from a poor spatial resolution. To resolve this issue, a lot of research efforts have been devoted to developing resolution enhancement methods which retrieve a high-resolution image out of its low-resolution degraded versions. In this paper, we consider a nonlocal total variation (NLTV) based super-resolution method which handles low-resolution images with geometric deformations. In particular, we apply the framework of alternating direction method of multipliers (ADMM) to deduce an effective algorithm, which involves soft thresholding and gradient descent. Effectiveness and robustness to noise of the proposed method are verified by various numerical experiments.
KW - Alternating direction method of multipliers (ADMM)
KW - Nonlocal total variation
KW - Remote sensing images
KW - Super-resolution image reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85101310023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101310023&partnerID=8YFLogxK
U2 - 10.1109/MicroRad49612.2020.9342611
DO - 10.1109/MicroRad49612.2020.9342611
M3 - Conference contribution
AN - SCOPUS:85101310023
T3 - 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings
BT - 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings
T2 - 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020
Y2 - 16 November 2020 through 20 November 2020
ER -