An effective super-resolution reconstruction method for geometrically deformed image sequences

Jing Qin; Igor Yanovsky

doi:10.1109/MicroRad49612.2020.9342611

An effective super-resolution reconstruction method for geometrically deformed image sequences

Jing Qin, Igor Yanovsky

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings
ISBN (Electronic)	9781728170930
DOIs	https://doi.org/10.1109/MicroRad49612.2020.9342611
State	Published - Nov 16 2020
Event	16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Virtual, Firenze, Italy Duration: Nov 16 2020 → Nov 20 2020

Publication series

Name	16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings

Conference

Conference	16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020
Country/Territory	Italy
City	Virtual, Firenze
Period	11/16/20 → 11/20/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Alternating direction method of multipliers (ADMM)
Nonlocal total variation
Remote sensing images
Super-resolution image reconstruction

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing
Control and Optimization
Instrumentation

Access to Document

10.1109/MicroRad49612.2020.9342611

Cite this

Qin, J., & Yanovsky, I. (2020). An effective super-resolution reconstruction method for geometrically deformed image sequences. In 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings Article 9342611 (16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings). https://doi.org/10.1109/MicroRad49612.2020.9342611

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Qin, J & Yanovsky, I 2020, An effective super-resolution reconstruction method for geometrically deformed image sequences. in 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings., 9342611, 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings, 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020, Virtual, Firenze, Italy, 11/16/20. https://doi.org/10.1109/MicroRad49612.2020.9342611

An effective super-resolution reconstruction method for geometrically deformed image sequences. / Qin, Jing; Yanovsky, Igor.
16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings. 2020. 9342611 (16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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Qin J, Yanovsky I. An effective super-resolution reconstruction method for geometrically deformed image sequences. In 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings. 2020. 9342611. (16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2020 - Proceedings). doi: 10.1109/MicroRad49612.2020.9342611

An effective super-resolution reconstruction method for geometrically deformed image sequences

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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