TY - JOUR
T1 - An Explicit and Scene-Adapted Definition of Convex Self-Similarity Prior with Application to Unsupervised Sentinel-2 Super-Resolution
AU - Lin, Chia Hsiang
AU - Bioucas-Dias, Jose M.
N1 - Funding Information:
Manuscript received May 1, 2019; revised August 10, 2019 and October 31, 2019; accepted November 6, 2019. Date of publication December 11, 2019; date of current version April 22, 2020. This work was supported by Fundação para a Ciência e a Tecnologia (FCT)-Portugal, Ministry of Education and Science (MEC) through national funds and co-funded by The European Regional Development Fund (FEDER) PT2020 Partnership Agreement under Project UID/EEA/50008/2019, in part the Young Scholar Fellowship Program (Einstein Program) of Ministry of Science and Technology (MOST), Taiwan, under Grant MOST108-2636-E-006-012, and in part by the Higher Education Sprout Project of Ministry of Education (MOE) to the Headquarters of University Advancement at National Cheng Kung University (NCKU). (Corresponding author: Chia-Hsiang Lin.) C.-H. Lin is with the Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: chiahsiang.steven.lin@ gmail.com).
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Sentinel-2 satellite, launched by the European Space Agency, plays a critical role in various Earth observation missions. However, the spatial resolutions of Sentinel-2 images are different across its spectral bands, including four bands with a resolution of 10 m, six bands with a resolution of 20 m, and three bands with a resolution of 60 m. To facilitate the effectiveness of analyzing these images, super-resolving of the low-/medium-resolution bands to a higher resolution is desired. As in any image restoration inverse problems, we exploit image self-similarity, a commonly observed property in natural images, which underlies the state-of-The-Art techniques, e.g., in image denoising. However, the design of self-similarity priors in nondiagonal inverse problems is challenging; often, a denoiser based on self-similarity is plugged into the iterations of an algorithm, without a guarantee of convergence in general. In this article, for the first time, we introduce a convex and scene-Adapted regularizer built explicitly on a self-similarity graph directly learned from the Sentinel-2 images. We then develop a fast algorithm, termed Sentinel-2 super-resolution via scene-Adapted self-similarity (SSSS). We experimentally show the superiority of SSSS over four commonly observed scenes, indicating the potential usage of our convex self-similarity regularization in other imaging inverse problems.
AB - Sentinel-2 satellite, launched by the European Space Agency, plays a critical role in various Earth observation missions. However, the spatial resolutions of Sentinel-2 images are different across its spectral bands, including four bands with a resolution of 10 m, six bands with a resolution of 20 m, and three bands with a resolution of 60 m. To facilitate the effectiveness of analyzing these images, super-resolving of the low-/medium-resolution bands to a higher resolution is desired. As in any image restoration inverse problems, we exploit image self-similarity, a commonly observed property in natural images, which underlies the state-of-The-Art techniques, e.g., in image denoising. However, the design of self-similarity priors in nondiagonal inverse problems is challenging; often, a denoiser based on self-similarity is plugged into the iterations of an algorithm, without a guarantee of convergence in general. In this article, for the first time, we introduce a convex and scene-Adapted regularizer built explicitly on a self-similarity graph directly learned from the Sentinel-2 images. We then develop a fast algorithm, termed Sentinel-2 super-resolution via scene-Adapted self-similarity (SSSS). We experimentally show the superiority of SSSS over four commonly observed scenes, indicating the potential usage of our convex self-similarity regularization in other imaging inverse problems.
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U2 - 10.1109/TGRS.2019.2953808
DO - 10.1109/TGRS.2019.2953808
M3 - Article
AN - SCOPUS:85084155939
SN - 0196-2892
VL - 58
SP - 3352
EP - 3365
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 5
M1 - 8931230
ER -