{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T10:33:37Z","timestamp":1772361217064,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801221"],"award-info":[{"award-number":["61801221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001229"],"award-info":[{"award-number":["62001229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101260"],"award-info":[{"award-number":["62101260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010011","name":"Jiangsu Postdoctoral Research Foundation","doi-asserted-by":"publisher","award":["2020Z441"],"award-info":[{"award-number":["2020Z441"]}],"id":[{"id":"10.13039\/501100010011","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold\/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.<\/jats:p>","DOI":"10.3390\/rs13204115","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T23:02:16Z","timestamp":1634252536000},"page":"4115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Ke","family":"Tan","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Xingyu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jianchao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Weimin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Hong","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6063","DOI":"10.3390\/rs5116063","article-title":"Autonomous navigation airborne forward-looking SAR high precision imaging with combination of pseudo-polar formatting and overlapped sub-aperture algorithm","volume":"5","author":"Peng","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xia, J., Lu, X., and Chen, W. 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