Image super-resolution based on conditional generative adversarial network
Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high-frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super-resolution. To alleviate this issue, the authors adopte...
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| Published in: | IET image processing Vol. 14; no. 13; pp. 3006 - 3013 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.11.2020
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| ISSN: | 1751-9659, 1751-9667 |
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| Abstract | Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high-frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super-resolution. To alleviate this issue, the authors adopted a conditional GAN (cGAN) in this study. The cGAN discriminator attempted to guess whether the unknown high-resolution (HR) image was produced by the generator with the aid of the original low-resolution (LR) image. They propose a novel discriminator that only penalises at the scale of the patch and, thus, has relatively few parameters to train. The generator of cGAN is an encoder–decoder with skip connections to shuttle the shared low-level information directly across the network. To better maintain the low-frequency information and recover the high-frequency information, they designed a generator loss function combining adversarial loss term and L1 loss term. The former term is beneficial to the synthesis of fine-grained textures, while the latter is responsible for learning the overall structure of the LR input. The experiments revealed that the proposed method could generate HR images with richer details and less over-smoothness. |
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| AbstractList | Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high‐frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super‐resolution. To alleviate this issue, the authors adopted a conditional GAN (cGAN) in this study. The cGAN discriminator attempted to guess whether the unknown high‐resolution (HR) image was produced by the generator with the aid of the original low‐resolution (LR) image. They propose a novel discriminator that only penalises at the scale of the patch and, thus, has relatively few parameters to train. The generator of cGAN is an encoder–decoder with skip connections to shuttle the shared low‐level information directly across the network. To better maintain the low‐frequency information and recover the high‐frequency information, they designed a generator loss function combining adversarial loss term and L1 loss term. The former term is beneficial to the synthesis of fine‐grained textures, while the latter is responsible for learning the overall structure of the LR input. The experiments revealed that the proposed method could generate HR images with richer details and less over‐smoothness. |
| Author | Chen, Zhanhong Gao, Hongxia Huang, Binyang Chen, Jiahe Li, Zhifu |
| Author_xml | – sequence: 1 givenname: Hongxia surname: Gao fullname: Gao, Hongxia organization: 1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510000, People's Republic of China – sequence: 2 givenname: Zhanhong surname: Chen fullname: Chen, Zhanhong organization: 1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510000, People's Republic of China – sequence: 3 givenname: Binyang surname: Huang fullname: Huang, Binyang email: scuthuangby@163.com organization: 1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510000, People's Republic of China – sequence: 4 givenname: Jiahe surname: Chen fullname: Chen, Jiahe organization: 2School of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102200, People's Republic of China – sequence: 5 givenname: Zhifu orcidid: 0000-0002-0382-7180 surname: Li fullname: Li, Zhifu organization: 3School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510000, People's Republic of China |
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| Cites_doi | 10.1109/CVPR.2017.19 10.1016/B978-012119792-6/50119-4 10.1109/CVPR.2016.182 10.1109/TIP.2017.2654163 10.1109/CVPR.2016.207 10.1109/TIP.2010.2050625 10.1109/TGRS.2018.2810208 10.1007/s00138-014-0623-4 10.1109/LGRS.2017.2736020 10.1109/CVPR.2018.00344 10.1109/CVPR.2018.00179 10.1109/LGRS.2016.2579661 10.1145/3343031.3351023 10.1109/CVPR.2016.278 10.1109/TPAMI.2015.2439281 10.1109/CVPR.2016.90 10.1007/978-3-319-46475-6_43 10.1109/TGRS.2012.2227329 10.1109/TGRS.2014.2307354 10.1109/ICCV.2013.75 10.1016/j.isprsjprs.2015.03.009 10.1007/978-3-319-10602-1_48 |
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| Keywords | adversarial loss term conditional GAN high-frequency information conditional generative adversarial network high-resolution image unsupervised learning HR images generator loss function cGAN discriminator L1 loss term low-frequency information image resolution neural nets image super-resolution low-resolution image |
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| References | Li, X.; Shen, H.; Zhang, L. (C2) 2014; 52 Wei, Y.; Yuan, Q.; Shen, H. (C15) 2017; 14 Nasrollahi, K.; Moeslund, B. (C1) 2014; 25 Dong, C.; Chen, C.L.; He, K. (C12) 2016; 38 Zhang, Z.; Li, F.; Zhao, M. (C10) 2017; 26 Li, J.; Yuan, Q.; Shen, H. (C6) 2016; 13 Zhang, Q.; Yuan, Q.; Zeng, C. (C14) 2016; 56 Li, X.; Shen, H.; Zhang, L. (C3) 2015; 106 Yang, J.; Wright, J.; Huang, T.S. (C5) 2010; 19 Lorenzi, L.; Melgani, F.; Mercier, G. (C7) 2013; 51 2017; 26 2010; 19 2017; 14 2013; 51 2008 2019 2014; 25 2018 2017 2016 2005 2015 2015; 106 2014 2013 2014; 52 2016; 38 2016; 13 2016; 56 Wang X. (e_1_2_6_22_1) 2016 e_1_2_6_10_1 e_1_2_6_31_1 e_1_2_6_30_1 Ioffe S. (e_1_2_6_24_1) 2015 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_14_1 e_1_2_6_11_1 e_1_2_6_12_1 Timofte R. (e_1_2_6_9_1) 2014 e_1_2_6_17_1 e_1_2_6_18_1 e_1_2_6_15_1 e_1_2_6_16_1 e_1_2_6_21_1 e_1_2_6_20_1 e_1_2_6_8_1 e_1_2_6_4_1 e_1_2_6_7_1 e_1_2_6_6_1 Denton E. (e_1_2_6_26_1) 2015 e_1_2_6_3_1 e_1_2_6_23_1 e_1_2_6_2_1 Yang J. (e_1_2_6_5_1) 2008 e_1_2_6_29_1 Goodfellow I.J. (e_1_2_6_25_1) 2014 e_1_2_6_28_1 e_1_2_6_27_1 |
| References_xml | – volume: 13 start-page: 1250 issue: 9 year: 2016 end-page: 1254 ident: C6 article-title: Hyperspectral image super-resolution by spectral mixture analysis and spatial–spectral group sparsity publication-title: IEEE Geosci. Remote Sens. – volume: 14 start-page: 1795 issue: 10 year: 2017 end-page: 1799 ident: C15 article-title: Boosting the accuracy of multispectral image pan-sharpening by learning a deep residual network publication-title: IEEE Geosci. Remote Sens. – volume: 52 start-page: 7086 issue: 11 year: 2014 end-page: 7098 ident: C2 article-title: Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 26 start-page: 1607 issue: 4 year: 2017 end-page: 1622 ident: C10 article-title: Robust neighborhood preserving projection by nuclear/L2, 1-norm regularization for image feature extraction publication-title: IEEE Trans. Image Process. – volume: 56 start-page: 4274 issue: 8 year: 2016 end-page: 4288 ident: C14 article-title: Missing data reconstruction in remote sensing image with a unified spatial–temporal–spectral deep convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 106 start-page: 1 year: 2015 end-page: 15 ident: C3 article-title: Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 38 start-page: 295 issue: 2 year: 2016 ident: C12 article-title: Image super-resolution using deep convolutional networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 19 start-page: 2861 issue: 11 year: 2010 end-page: 2873 ident: C5 article-title: Image super-resolution via sparse representation publication-title: IEEE Trans. Image Process. – volume: 25 start-page: 1423 issue: 6 year: 2014 end-page: 1468 ident: C1 article-title: Super-resolution: a comprehensive survey publication-title: Mach. Vis. Appl. – volume: 51 start-page: 3998 issue: 7 year: 2013 end-page: 4008 ident: C7 article-title: Missing-area reconstruction in multispectral images under a compressive sensing perspective publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 19 start-page: 2861 issue: 11 year: 2010 end-page: 2873 article-title: Image super‐resolution via sparse representation publication-title: IEEE Trans. Image Process. – start-page: 770 year: 2016 end-page: 778 – start-page: 18 year: 2005 – start-page: 1664 year: 2018 end-page: 1673 – volume: 26 start-page: 1607 issue: 4 year: 2017 end-page: 1622 article-title: Robust neighborhood preserving projection by nuclear/L2, 1‐norm regularization for image feature extraction publication-title: IEEE Trans. Image Process. – volume: 25 start-page: 1423 issue: 6 year: 2014 end-page: 1468 article-title: Super‐resolution: a comprehensive survey publication-title: Mach. Vis. Appl. – volume: 51 start-page: 3998 issue: 7 year: 2013 end-page: 4008 article-title: Missing‐area reconstruction in multispectral images under a compressive sensing perspective publication-title: IEEE Trans. Geosci. Remote Sens. – year: 2016 – volume: 14 start-page: 1795 issue: 10 year: 2017 end-page: 1799 article-title: Boosting the accuracy of multispectral image pan‐sharpening by learning a deep residual network publication-title: IEEE Geosci. Remote Sens. – year: 2014 – volume: 52 start-page: 7086 issue: 11 year: 2014 end-page: 7098 article-title: Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 1874 year: 2016 end-page: 1883 – start-page: 3262 year: 2018 end-page: 3271 – volume: 56 start-page: 4274 issue: 8 year: 2016 end-page: 4288 article-title: Missing data reconstruction in remote sensing image with a unified spatial–temporal–spectral deep convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 1 year: 2008 end-page: 8 – start-page: 561 year: 2013 end-page: 568 – start-page: 318 year: 2016 end-page: 335 – volume: 106 start-page: 1 year: 2015 end-page: 15 article-title: Sparse‐based reconstruction of missing information in remote sensing images from spectral/temporal complementary information publication-title: ISPRS J. Photogramm. Remote Sens. – start-page: 694 year: 2016 end-page: 711 – start-page: 105 year: 2017 end-page: 114 – start-page: 2672 year: 2014 end-page: 2680 – start-page: 740 year: 2014 end-page: 755 – start-page: 2536 year: 2016 end-page: 2544 – volume: 13 start-page: 1250 issue: 9 year: 2016 end-page: 1254 article-title: Hyperspectral image super‐resolution by spectral mixture analysis and spatial–spectral group sparsity publication-title: IEEE Geosci. Remote Sens. – start-page: 448 year: 2015 end-page: 456 – volume: 38 start-page: 295 issue: 2 year: 2016 article-title: Image super‐resolution using deep convolutional networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 1486 year: 2015 end-page: 1494 – year: 2019 – start-page: 111 year: 2014 end-page: 126 – start-page: 1646 year: 2016 end-page: 1654 – ident: e_1_2_6_19_1 doi: 10.1109/CVPR.2017.19 – ident: e_1_2_6_31_1 doi: 10.1016/B978-012119792-6/50119-4 – ident: e_1_2_6_20_1 – ident: e_1_2_6_14_1 doi: 10.1109/CVPR.2016.182 – start-page: 1486 volume-title: Advances in Neural Information Processing Systems (NIPS) year: 2015 ident: e_1_2_6_26_1 – ident: e_1_2_6_11_1 doi: 10.1109/TIP.2017.2654163 – start-page: 1 volume-title: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) year: 2008 ident: e_1_2_6_5_1 – ident: e_1_2_6_18_1 doi: 10.1109/CVPR.2016.207 – ident: e_1_2_6_28_1 – start-page: 318 volume-title: Euro. Conf. on Computer Vision (ECCV) year: 2016 ident: e_1_2_6_22_1 – ident: e_1_2_6_6_1 doi: 10.1109/TIP.2010.2050625 – ident: e_1_2_6_15_1 doi: 10.1109/TGRS.2018.2810208 – ident: e_1_2_6_2_1 doi: 10.1007/s00138-014-0623-4 – ident: e_1_2_6_16_1 doi: 10.1109/LGRS.2017.2736020 – ident: e_1_2_6_29_1 doi: 10.1109/CVPR.2018.00344 – ident: e_1_2_6_30_1 doi: 10.1109/CVPR.2018.00179 – ident: e_1_2_6_7_1 doi: 10.1109/LGRS.2016.2579661 – ident: e_1_2_6_12_1 doi: 10.1145/3343031.3351023 – start-page: 2672 volume-title: Advances in Neural Information Processing Systems (NIPS) year: 2014 ident: e_1_2_6_25_1 – ident: e_1_2_6_21_1 doi: 10.1109/CVPR.2016.278 – ident: e_1_2_6_13_1 doi: 10.1109/TPAMI.2015.2439281 – start-page: 448 volume-title: Int. Conf. on Machine Learning (ICML) year: 2015 ident: e_1_2_6_24_1 – ident: e_1_2_6_17_1 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_6_23_1 doi: 10.1007/978-3-319-46475-6_43 – ident: e_1_2_6_8_1 doi: 10.1109/TGRS.2012.2227329 – ident: e_1_2_6_3_1 doi: 10.1109/TGRS.2014.2307354 – ident: e_1_2_6_10_1 doi: 10.1109/ICCV.2013.75 – ident: e_1_2_6_4_1 doi: 10.1016/j.isprsjprs.2015.03.009 – ident: e_1_2_6_27_1 doi: 10.1007/978-3-319-10602-1_48 – start-page: 111 volume-title: Asian Conf. on Computer Vision (ACCV) year: 2014 ident: e_1_2_6_9_1 |
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| Snippet | Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high-frequency details. However, a mismatch... Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high‐frequency details. However, a mismatch... |
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| SubjectTerms | adversarial loss term cGAN discriminator conditional GAN conditional generative adversarial network generator loss function high‐frequency information high‐resolution image HR images image resolution image super‐resolution L1 loss term low‐frequency information low‐resolution image neural nets Research Article unsupervised learning |
| Title | Image super-resolution based on conditional generative adversarial network |
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