Self-Supervised multi-image Super-Resolution Network for Space Image Restoration
Abstract Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring space targets. During actual missions, however, bursts of push-frame imaging methods, dynamic noise during observation, large motions and ot...
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Published in | Journal of physics. Conference series Vol. 2347; no. 1; pp. 12021 - 12026 |
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Abstract | Abstract
Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring space targets. During actual missions, however, bursts of push-frame imaging methods, dynamic noise during observation, large motions and other factors can result in visible degradation, and it isn’t easy to have the corresponding high-resolution (HR) images for the low-resolution (LR) images acquired at the high-speed intersection, which seriously limits the existing multi-image super-resolution (MISR) methods for space-based applications. To overcome these problems, this paper proposes a self-supervised video super-resolution deep learning method that can be trained end-to-end in space-based observations without LR/HR image pairs. We generate extra training pairs using different sizes of sampling factors for LR videos and train the video hyper-resolution network from coarse to fine using a pyramid format. Extensive experiments on the public satellite dataset BUAA-SID-share1.0 show that our approach outperforms traditional video hyper-resolution methods. |
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AbstractList | Abstract
Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring space targets. During actual missions, however, bursts of push-frame imaging methods, dynamic noise during observation, large motions and other factors can result in visible degradation, and it isn’t easy to have the corresponding high-resolution (HR) images for the low-resolution (LR) images acquired at the high-speed intersection, which seriously limits the existing multi-image super-resolution (MISR) methods for space-based applications. To overcome these problems, this paper proposes a self-supervised video super-resolution deep learning method that can be trained end-to-end in space-based observations without LR/HR image pairs. We generate extra training pairs using different sizes of sampling factors for LR videos and train the video hyper-resolution network from coarse to fine using a pyramid format. Extensive experiments on the public satellite dataset BUAA-SID-share1.0 show that our approach outperforms traditional video hyper-resolution methods. Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring space targets. During actual missions, however, bursts of push-frame imaging methods, dynamic noise during observation, large motions and other factors can result in visible degradation, and it isn’t easy to have the corresponding high-resolution (HR) images for the low-resolution (LR) images acquired at the high-speed intersection, which seriously limits the existing multi-image super-resolution (MISR) methods for space-based applications. To overcome these problems, this paper proposes a self-supervised video super-resolution deep learning method that can be trained end-to-end in space-based observations without LR/HR image pairs. We generate extra training pairs using different sizes of sampling factors for LR videos and train the video hyper-resolution network from coarse to fine using a pyramid format. Extensive experiments on the public satellite dataset BUAA-SID-share1.0 show that our approach outperforms traditional video hyper-resolution methods. |
Author | Zhou, Junyi Cao, Shenyi Wang, Canyu Ren, Xiaoyuan Wang, Zhuang |
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Cites_doi | 10.1109/CVPRW.2019.00247 10.1109/CVPR.2018.00340 10.1109/TPAMI.2013.127 10.1007/s00138-014-0623-4 |
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References | Wang (JPCS_2347_1_012021bib3) 2019 Jo (JPCS_2347_1_012021bib17) 2018 Zhang (JPCS_2347_1_012021bib14) 2010 Lehtinen (JPCS_2347_1_012021bib10) 2018 Tian (JPCS_2347_1_012021bib8) 2018 Liu (JPCS_2347_1_012021bib15) 2014; 36 Chan (JPCS_2347_1_012021bib9) 2020 Xue (JPCS_2347_1_012021bib18) 2017 Jo (JPCS_2347_1_012021bib2) 2018 Yuan (JPCS_2347_1_012021bib11) 2018 Merino (JPCS_2347_1_012021bib5) 2007; 45 Dong (JPCS_2347_1_012021bib16) 2014 Maeda (JPCS_2347_1_012021bib13) 2020 Yi (JPCS_2347_1_012021bib4) 2019 Agustsson (JPCS_2347_1_012021bib7) 2017 Kim (JPCS_2347_1_012021bib12) 2020 Nasrollahi (JPCS_2347_1_012021bib1) 2014; 25 Latry (JPCS_2347_1_012021bib6) 2000; 5 |
References_xml | – volume: 45 start-page: 1446 year: 2007 ident: JPCS_2347_1_012021bib5 article-title: Super-resolution of remotely sensed images with variable-pixel linear reconstruction publication-title: IEEE TGRS contributor: fullname: Merino – start-page: 3106 year: 2019 ident: JPCS_2347_1_012021bib4 article-title: Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations contributor: fullname: Yi – start-page: 456 year: 2020 ident: JPCS_2347_1_012021bib12 article-title: Unsupervised real-world super resolution with cycle generative adversarial network and domain discriminator contributor: fullname: Kim – year: 2020 ident: JPCS_2347_1_012021bib9 article-title: Understanding Deformable Alignment in Video Super-Resolution[C] contributor: fullname: Chan – year: 2010 ident: JPCS_2347_1_012021bib14 article-title: BUAA-SID1.0 Space Object Image Dataset contributor: fullname: Zhang – volume: 5 start-page: 2322 year: 2000 ident: JPCS_2347_1_012021bib6 contributor: fullname: Latry – start-page: 701 year: 2018 ident: JPCS_2347_1_012021bib11 article-title: Unsupervised image superresolution using cycle-in-cycle generative adversarial networks contributor: fullname: Yuan – year: 2019 ident: JPCS_2347_1_012021bib3 article-title: Edvr: Video restoration with enhanced deformable convolutional networks. In doi: 10.1109/CVPRW.2019.00247 contributor: fullname: Wang – start-page: 126 year: 2017 ident: JPCS_2347_1_012021bib7 article-title: Ntire 2017 challenge on single image super-resolution: Dataset and study contributor: fullname: Agustsson – year: 2018 ident: JPCS_2347_1_012021bib17 article-title: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation doi: 10.1109/CVPR.2018.00340 contributor: fullname: Jo – start-page: 184 year: 2014 ident: JPCS_2347_1_012021bib16 contributor: fullname: Dong – start-page: 3224 year: 2018 ident: JPCS_2347_1_012021bib2 article-title: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation contributor: fullname: Jo – year: 2018 ident: JPCS_2347_1_012021bib10 article-title: Noise2noise: Learning image restoration without clean data contributor: fullname: Lehtinen – volume: 36 start-page: 346 year: 2014 ident: JPCS_2347_1_012021bib15 article-title: On bayesian adaptive video super resolution publication-title: TPAMI doi: 10.1109/TPAMI.2013.127 contributor: fullname: Liu – start-page: 291 year: 2020 ident: JPCS_2347_1_012021bib13 article-title: Unpaired image super-resolution using pseudo-supervision contributor: fullname: Maeda – year: 2018 ident: JPCS_2347_1_012021bib8 article-title: TDAN: Temporally deformable alignment network for video super-resolution contributor: fullname: Tian – volume: 25 start-page: 1423 year: 2014 ident: JPCS_2347_1_012021bib1 article-title: Super-resolution: a comprehensive survey publication-title: Machine vision and applications doi: 10.1007/s00138-014-0623-4 contributor: fullname: Nasrollahi – year: 2017 ident: JPCS_2347_1_012021bib18 article-title: Video enhancement with task-oriented flow contributor: fullname: Xue |
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Snippet | Abstract
Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring... Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring space... |
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StartPage | 12021 |
SubjectTerms | deep learning deformable convolutional Image acquisition Image resolution Image restoration multi-image super-resolution Physics Self-supervised learning Space applications space object Space surveillance |
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Title | Self-Supervised multi-image Super-Resolution Network for Space Image Restoration |
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