Robust super-resolution image reconstruction method for geometrically deformed remote sensing images

Jing Qin; Igor Yanovsky

doi:10.1109/IGARSS.2018.8518056

Robust super-resolution image reconstruction method for geometrically deformed remote sensing images

Jing Qin
, Igor Yanovsky

Mathematics

Producción científica: Conference contribution › revisión exhaustiva

7 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Páginas	8050-8053
Número de páginas	4
ISBN (versión digital)	9781538671504
DOI	https://doi.org/10.1109/IGARSS.2018.8518056
Estado	Published - oct 31 2018
Evento	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duración: jul 22 2018 → jul 27 2018

Serie de la publicación

Nombre	International Geoscience and Remote Sensing Symposium (IGARSS)
Volumen	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
País/Territorio	Spain
Ciudad	Valencia
Período	7/22/18 → 7/27/18

Nota bibliográfica

Publisher Copyright:
© 2018 IEEE

Financiación

The authors would like to thank Stamatis Lefkimmiatis from Skolkovo Institute of Science and Technology for providing advice on efficiently implementing the NLTV regularization.

Financiadores
Akademiet for de Tekniske Videnskaber

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

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10.1109/IGARSS.2018.8518056

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Qin, J., & Yanovsky, I. (2018). Robust super-resolution image reconstruction method for geometrically deformed remote sensing images. En 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 8050-8053). Artículo 8518056 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). https://doi.org/10.1109/IGARSS.2018.8518056

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title = "Robust super-resolution image reconstruction method for geometrically deformed remote sensing images",

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.",

keywords = "Primal-dual algorithm, Remote sensing images, Super-resolution image reconstruction",

author = "Jing Qin and Igor Yanovsky",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

year = "2018",

month = oct,

day = "31",

doi = "10.1109/IGARSS.2018.8518056",

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Qin, J & Yanovsky, I 2018, Robust super-resolution image reconstruction method for geometrically deformed remote sensing images. En 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518056, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, pp. 8050-8053, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 7/22/18. https://doi.org/10.1109/IGARSS.2018.8518056

Robust super-resolution image reconstruction method for geometrically deformed remote sensing images. / Qin, Jing; Yanovsky, Igor.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. 2018. p. 8050-8053 8518056 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

Producción científica: Conference contribution › revisión exhaustiva

TY - GEN

T1 - Robust super-resolution image reconstruction method for geometrically deformed remote sensing images

AU - Qin, Jing

AU - Yanovsky, Igor

PY - 2018/10/31

Y1 - 2018/10/31

N2 - 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.

AB - 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.

KW - Primal-dual algorithm

KW - Remote sensing images

KW - Super-resolution image reconstruction

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M3 - Conference contribution

AN - SCOPUS:85063138583

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SP - 8050

EP - 8053

BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

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Robust super-resolution image reconstruction method for geometrically deformed remote sensing images

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Serie de la publicación

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Financiación

ASJC Scopus subject areas

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