{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:04:10Z","timestamp":1780542250747,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2014,1,6]],"date-time":"2014-01-06T00:00:00Z","timestamp":1388966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by  the super-resolution reconstruction technique. However, different sensor shooting angles lead to different resolutions for each angle image, which affects the effectiveness of the  super-resolution reconstruction of the multi-angle images. In view of this, we propose utilizing adaptive weighted super-resolution reconstruction to alleviate the limitations of the different resolutions. This paper employs two adaptive weighting themes. The first approach uses the angle between the imaging angle of the current image and that of the nadir image. The second is closely related to the residual error of each low-resolution angle image. The experimental results confirm the feasibility of the proposed method and demonstrate the effectiveness of the proposed adaptive weighted super-resolution approach.<\/jats:p>","DOI":"10.3390\/rs6010637","type":"journal-article","created":{"date-parts":[[2014,1,6]],"date-time":"2014-01-06T11:17:08Z","timestamp":1389007028000},"page":"637-657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences"],"prefix":"10.3390","volume":"6","author":[{"given":"Hongyan","family":"Zhang","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Xuzhou Institute of Surveying and Mapping, Xuzhou 221003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangpei","family":"Zhang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanfeng","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2014,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ng, M.K., Shen, H., Lam, E., and Zhang, L. (2007). A total variation based super-resolution reconstruction algorithm for digital video. EURASIP J. Adv. Signal Process.","DOI":"10.1155\/2007\/74585"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/j.sigpro.2012.01.020","article-title":"A super-resolution reconstruction algorithm for hyperspectral images","volume":"92","author":"Zhang","year":"2012","journal-title":"Signal Process"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-resolution image reconstruction: A technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. Mag"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1002\/ima.20007","article-title":"Advances and challenges in super-resolution","volume":"14","author":"Farsiu","year":"2004","journal-title":"Int. J. Imaging Syst. Technol"},{"key":"ref_5","first-page":"317","article-title":"Multiframe image restoration and registration","volume":"1","author":"Tsai","year":"1984","journal-title":"Adv. Comput. Vis. Image Process"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/83.503915","article-title":"Extraction of high-resolution frames from video sequences","volume":"5","author":"Schultz","year":"1996","journal-title":"IEEE Trans. Image Process"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1364\/JOSAA.6.001715","article-title":"High-resolution image recovery from image-plane arrays, using convex projections","volume":"6","author":"Stark","year":"1989","journal-title":"JOSA A"},{"key":"ref_8","first-page":"181","article-title":"Improved resolution from subpixel shifted pictures","volume":"54","author":"Ur","year":"1992","journal-title":"CVGIP: Graph. Models Image Process"},{"key":"ref_9","unstructured":"Tom, B.C., and Katsaggelos, A.K. (1995, January 23\u201326). Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images. Washington, DC, USA."},{"key":"ref_10","unstructured":"Tom, B.C., Katsaggelos, A.K., and Galatsanos, N.P. (1994, January 13\u201316). Reconstruction of a High Resolution Image from Registration and Restoration of Low Resolution Images. Austin, TX, USA."},{"key":"ref_11","first-page":"231","article-title":"Improving resolution by image registration","volume":"53","author":"Irani","year":"1991","journal-title":"CVGIP: Graph. Models Image Process"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/83.650118","article-title":"Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images","volume":"6","author":"Elad","year":"1997","journal-title":"IEEE Trans. Image Process"},{"key":"ref_13","unstructured":"Latry, C., and Rouge, B. (2003, January 21\u201325). Super resolution: Quincunx Sampling and Fusion Processing. Toulouse, France."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TIP.2005.854479","article-title":"Super-resolution reconstruction of hyperspectral images","volume":"14","author":"Akgun","year":"2005","journal-title":"IEEE Trans. Image Process"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1093\/comjnl\/bxm028","article-title":"Super-resolution reconstruction algorithm to MODIS remote sensing images","volume":"52","author":"Shen","year":"2009","journal-title":"Comput. J"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1109\/TGRS.2009.2031636","article-title":"Super resolution for remote sensing images based on a universal hidden markov tree model","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1109\/TGRS.2009.2039797","article-title":"Superresolution enhancement of hyperspectral CHRIS\/Proba images with a thin-plate spline nonrigid transform model","volume":"48","author":"Chan","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/JSTARS.2011.2182505","article-title":"An operational superresolution approach for multi-temporal and multi-angle remotely sensed imagery","volume":"5","author":"Ma","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1109\/TGRS.2005.853569","article-title":"Resolution enhancement of multilook imagery for the multispectral thermal imager","volume":"43","author":"Galbraith","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TIP.2004.834669","article-title":"Fast and robust multiframe super resolution","volume":"13","author":"Farsiu","year":"2004","journal-title":"IEEE Trans. Image Process"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vandewalle, P., Susstrunk, S., and Vetterli, M. (2006). A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP J. Adv. Signal Process.","DOI":"10.1155\/ASP\/2006\/71459"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Physica D"},{"key":"ref_23","unstructured":"Bioucas-Dias, J., Figueiredo, M., and Oliveira, J. (2006, January 4\u20138). Adaptive Bayesian\/total-variation image deconvolution: A majorization-minimization approach. Florence, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Vogel, C.R. (2002). Computational Methods for Inverse Problems, SIAM Frontiers in Applied Mathematics.","DOI":"10.1137\/1.9780898717570"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1109\/TIP.2005.860599","article-title":"An image super-resolution algorithm for different error levels per frame","volume":"15","author":"Hu","year":"2006","journal-title":"IEEE Trans. Image Process"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pacifici, F., Chanussot, J., and Du, Q. (2011, January 24\u201329). GRSS data fusion contest: Exploiting WorldView-2 multi-angular acquisitions. Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049404"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/79.581363","article-title":"Digital image restoration","volume":"14","author":"Banham","year":"1997","journal-title":"IEEE Signal Process. Mag"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2678","DOI":"10.1109\/TIP.2011.2131660","article-title":"A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)","volume":"20","author":"Narvekar","year":"2011","journal-title":"IEEE Trans. Image Process"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/TIP.2010.2052820","article-title":"Automatic parameter selection for denoising algorithms using a no-reference measure of image content","volume":"19","author":"Xiang","year":"2010","journal-title":"IEEE Trans. Image Process"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.imavis.2008.05.010","article-title":"A soft MAP framework for blind super-resolution image reconstruction","volume":"27","author":"He","year":"2009","journal-title":"Image Vis. Comput"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.sigpro.2009.09.002","article-title":"A super-resolution reconstruction algorithm for surveillance images","volume":"90","author":"Zhang","year":"2010","journal-title":"Signal Process"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/1\/637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:07:07Z","timestamp":1760216827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/1\/637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,1,6]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2014,1]]}},"alternative-id":["rs6010637"],"URL":"https:\/\/doi.org\/10.3390\/rs6010637","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1,6]]}}}