{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T15:31:20Z","timestamp":1767108680388,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000271","name":"Science and Technology Facilities Council","doi-asserted-by":"publisher","award":["ST\/K000977\/1","STFC KEI2019-03-01"],"award-info":[{"award-number":["ST\/K000977\/1","STFC KEI2019-03-01"]}],"id":[{"id":"10.13039\/501100000271","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011690","name":"UK Space Agency","doi-asserted-by":"publisher","award":["RP10G0435A05","RP10G0435C206","ST\/S001891\/1"],"award-info":[{"award-number":["RP10G0435A05","RP10G0435C206","ST\/S001891\/1"]}],"id":[{"id":"10.13039\/100011690","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We introduce a robust and light-weight multi-image super-resolution restoration (SRR) method and processing system, called OpTiGAN, using a combination of a multi-image maximum a posteriori approach and a deep learning approach. We show the advantages of using a combined two-stage SRR processing scheme for significantly reducing inference artefacts and improving effective resolution in comparison to other SRR techniques. We demonstrate the optimality of OpTiGAN for SRR of ultra-high-resolution satellite images and video frames from 31 cm\/pixel WorldView-3, 75 cm\/pixel Deimos-2 and 70 cm\/pixel SkySat. Detailed qualitative and quantitative assessments are provided for the SRR results on a CEOS-WGCV-IVOS geo-calibration and validation site at Baotou, China, which features artificial permanent optical targets. Our measurements have shown a 3.69 times enhancement of effective resolution from 31 cm\/pixel WorldView-3 imagery to 9 cm\/pixel SRR.<\/jats:p>","DOI":"10.3390\/rs13122269","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T21:34:38Z","timestamp":1623360878000},"page":"2269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Super-Resolution Restoration of Spaceborne Ultra-High-Resolution Images Using the UCL OpTiGAN System"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-6655","authenticated-orcid":false,"given":"Yu","family":"Tao","sequence":"first","affiliation":[{"name":"Imaging Group, Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, Holmbury St Mary, Surrey RH5 6NT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5077-3736","authenticated-orcid":false,"given":"Jan-Peter","family":"Muller","sequence":"additional","affiliation":[{"name":"Imaging Group, Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, Holmbury St Mary, Surrey RH5 6NT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tao, Y., and Muller, J.P. (2019). Super-resolution restoration of MISR images using the UCL MAGiGAN system. Remote Sens., 11.","DOI":"10.1117\/12.2532889"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3795","DOI":"10.1016\/j.patcog.2012.03.023","article-title":"Progressively weighted affine adaptive correlation matching for quasi-dense 3D reconstruction","volume":"45","author":"Shin","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.pss.2015.11.010","article-title":"A novel method for surface exploration: Super-resolution restoration of Mars repeat-pass orbital imagery","volume":"121","author":"Tao","year":"2016","journal-title":"Planet. Space Sci."},{"key":"ref_4","first-page":"4","article-title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","volume":"2","author":"Ledig","year":"2017","journal-title":"CVPR"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/3-540-45103-X_50","article-title":"Two-frame motion estimation based on polynomial expansion","volume":"2749","author":"Farneback","year":"2003","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TIP.2004.834669","article-title":"Fast and robust multi-frame super-resolution","volume":"13","author":"Farsiu","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cullingworth, C., and Muller, J.-P. (2021). Contemporaneous Monitoring of the Whole Dynamic Earth System from Space, Part I: System Simulation Study Using GEO and Molniya Orbits. Remote Sens., 13.","DOI":"10.3390\/rs13050878"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/0167-8655(87)90067-5","article-title":"Improving image resolution using subpixel motion","volume":"5","author":"Peleg","year":"1987","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","unstructured":"Keren, D., Peleg, S., and Brada, R. (1988, January 5\u20139). Image sequence enhancement using subpixel displacements. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Ann Arbor, MI, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bascle, B., Blake, A., and Zisserman, A. (1996, January 15\u201318). Motion deblurring and super-resolution from an image sequence. Proceedings of the 4th European Conference on Computer Vision, Cambridge, UK.","DOI":"10.1007\/3-540-61123-1_171"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1049\/ip-vis:20000333","article-title":"Polyphase back-projection filtering for image resolution enhancement","volume":"147","author":"Cohen","year":"2000","journal-title":"IEE Proc. Vis. Image Signal Process."},{"key":"ref_12","unstructured":"Zomet, A., Rav-Acha, A., and Peleg, S. (2001, January 8\u201314). Robust super-resolution. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA."},{"key":"ref_13","unstructured":"Luttrell, S.P. (1990, January 9). Bayesian autofocus\/super-resolution theory. In Proceedings of IEE Colloquium on Role of Image Processing in Defence and Military Electronics, London, UK."},{"key":"ref_14","unstructured":"Cheeseman, P., Kanefsky, B., Kraft, R., and Stutz, J. (1994). Super-Resolved Surface Reconstruction from Multiple Images, NASA. Technical Report FIA9412."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/83.287017","article-title":"A Bayesian Approach to Image Expansion for Improved De_nition","volume":"3","author":"Schultz","year":"1994","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1049\/el:19990543","article-title":"E_cient method for improving Poisson MAP super-resolution","volume":"35","author":"Pan","year":"1999","journal-title":"Electron Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/83.935034","article-title":"A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur","volume":"10","author":"Elad","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1109\/83.650116","article-title":"Joint MAP registration and high resolution image estimation using a sequence of undersampled images","volume":"6","author":"Hardie","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1006\/jvci.1997.0370","article-title":"Subpixel motion estimation for super-resolution image sequence enhancement","volume":"9","author":"Schultz","year":"1998","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_20","unstructured":"Borman, S., and Stevenson, R.L. (1999, January 24\u201328). Simultaneous multi-frame MAP super-resolution video enhancement using spatio temporal priors. In Proceedings of IEEE International Conference on Image Processing, Kobe, Japan."},{"key":"ref_21","unstructured":"Pickup, L., Roberts, S., and Zisserman, A. (2003, January 8\u201313). A sampled texture prior for image super-resolution. In Proceeding of the 16th International conference on Advances in Neural Information Processing Systems, Vancouver, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/TIP.2011.2106793","article-title":"Video super-resolution using simultaneous motion and intensity calculations","volume":"20","author":"Keller","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TCSVT.2011.2163447","article-title":"Multiframe super-resolution employing a spatially weighted total variation model","volume":"22","author":"Yuan","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4029","DOI":"10.1109\/TIP.2012.2201492","article-title":"Super resolution image reconstruction through Bregman iteration using morphologic regularization","volume":"21","author":"Purkait","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4054","DOI":"10.1109\/TIP.2012.2199330","article-title":"Generative Bayesian image super resolution with natural image prior","volume":"21","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a deep convolutional network for image super-resolution. Proceedings of the ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_27","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (July, January 26). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., and Tang, X. (2016). Accelerating the super-resolution convolutional neural network. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref_29","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (July, January 26). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_30","first-page":"02358","article-title":"Lightweight image super-resolution with adaptive weighted learning network","volume":"1904","author":"Wang","year":"2019","journal-title":"Arxiv Prepr. Arxiv"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Scholkopf, B., and Hirsch, M. (2017, January 22\u201329). EnhanceNet: Single image super-resolution through automated texture synthesis. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.481"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Change Loy, C. (2018, January 8\u201314). ESRGAN: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rakotonirina, N.C., and Rasoanaivo, A. (2020, January 4). ESRGAN+: Further improving enhanced super-resolution generative adversarial network. Proceedings of the InICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054071"},{"key":"ref_34","unstructured":"Zhang, W., Liu, Y., Dong, C., and Qiao, Y. (November, January 27). RankSRGAN: Generative adversarial networks with ranker for image super-resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_36","unstructured":"Yu, J., Fan, Y., Yang, J., Xu, N., Wang, Z., Wang, X., and Huang, T. (2018). Wide activation for efficient and accurate image super-resolution. Arxiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., and Sohn, K.A. (2018, January 8\u201314). Fast, accurate, and lightweight super-resolution with cascading residual network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"ref_38","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_40","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (July, January 26). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., and Liu, X. (2017, January 21\u201326). Image super-resolution via deep recursive residual network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z.S., Wang, L.W., Li, C.T., Siu, W.C., and Chan, Y.L. (2019, January 27\u201328). Image super-resolution via attention based back projection networks. In Proceedings of 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00436"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tao, Y., Conway, S.J., Muller, J.-P., Putri, A.R.D., Thomas, N., and Cremonese, G. (2021). Single Image Super-Resolution Restoration of TGO CaSSIS colour images: Demonstration with Perseverance Rover Landing Site and Mars science targets. Remote Sens., 13.","DOI":"10.3390\/rs13091777"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tao, Y., and Muller, J.-P. (2018, January 10\u201313). Repeat multiview panchromatic super-resolution restoration using the UCL MAGiGAN system. Proceedings of the Image and Signal Processing for Remote Sensing XXIV 2018, Berlin, Germany. Issue 3.","DOI":"10.1117\/12.2500196"},{"key":"ref_45","unstructured":"Jolicoeur-Martineau, A. (2018). The relativistic discriminator: A key element missing from standard GAN. Arxiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-Reference Image Quality Assessment in the Spatial Domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Venkatanath, N., Praneeth, D., Chandrasekhar, B.M., Channappayya, S.S., and Medasani, S.S. (March, January 27). Blind Image Quality Evaluation Using Perception Based Features. Proceedings of the 21st National Conference on Communications (NCC) 2015, Mumbai, India.","DOI":"10.1109\/NCC.2015.7084843"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.5194\/isprsarchives-XL-7-W3-1233-2015","article-title":"Comprehensive calibration and validation site for information remote sensing","volume":"40","author":"Li","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/LGRS.2016.2632181","article-title":"A permanent bar pattern distributed target for microwave image resolution analysis","volume":"14","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:12:47Z","timestamp":1760163167000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":49,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122269"],"URL":"https:\/\/doi.org\/10.3390\/rs13122269","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,6,10]]}}}