{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:56:29Z","timestamp":1780635389799,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T00:00:00Z","timestamp":1571011200000},"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":["61771351"],"award-info":[{"award-number":["61771351"]}],"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":["61871335"],"award-info":[{"award-number":["61871335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CETC key laboratory of aerospace information applications","award":["SXX18629T022"],"award-info":[{"award-number":["SXX18629T022"]}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2018CFA006"],"award-info":[{"award-number":["2018CFA006"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Despeckling is a longstanding topic in synthetic aperture radar (SAR) images. Recently, many convolutional neural network (CNN) based methods have been proposed and shown state-of-the-art performance for SAR despeckling problem. However, these CNN based methods always need many training data or can only deal with specific noise level. To solve these problems, we directly embed an efficient CNN pre-trained model for additive white Gaussian noise (AWGN) with Multi-channel Logarithm with Gaussian denoising (MuLoG) algorithm to deal with the multiplicative noise in SAR images. This flexible pre-trained CNN model takes the noise level as input, thus only a single pre-trained model is needed to deal with different noise levels. We also use a detector to find the homogeneous region automatically to estimate the noise level of image as input. Embedded with MuLoG, our proposed filter can despeckle not only single channel but also multi-channel SAR images. Finally, both simulated and real (Pol)SAR images were tested in experiments, and the results show that the proposed method has better and more robust performance than others.<\/jats:p>","DOI":"10.3390\/rs11202379","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T12:14:05Z","timestamp":1571055245000},"page":"2379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4199-4756","authenticated-orcid":false,"given":"Ting","family":"Pan","sequence":"first","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-6647","authenticated-orcid":false,"given":"Dong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-8768","authenticated-orcid":false,"given":"Wen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"},{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9735-570X","authenticated-orcid":false,"given":"Heng-Chao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,14]]},"reference":[{"key":"ref_1","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Application, CRC Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1364\/JOSA.66.001145","article-title":"Some fundamental properties of speckle","volume":"66","author":"Goodman","year":"1976","journal-title":"J. Opt. Soc. Am."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2277512","article-title":"A tutorial on speckle reduction in synthetic aperture radar images","volume":"1","author":"Argenti","year":"2003","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/MSP.2014.2311305","article-title":"Exploiting patch similarity for sar image processing: the nonlocal paradigm","volume":"31","author":"Deledalle","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital image enhancement and noise filtering by use of local statistics","volume":"2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/S0146-664X(81)80018-4","article-title":"Refined filtering of image noise using local statistics","volume":"15","author":"Lee","year":"1981","journal-title":"Comput. Gr. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TPAMI.1982.4767223","article-title":"A model for radar images and its application to adaptive digital filtering of multiplicative noise","volume":"4","author":"Frost","year":"1982","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1985.4767641","article-title":"Adaptive noise smoothing filter for images with signal-dependent noise","volume":"7","author":"Kuan","year":"1985","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Lop\u00e9s, A., Nezry, E., Touzi, R., and Laur, H. (1990, January 20\u201324). Maximum a posteriori speckle filtering and first order texture models in SAR images. Proceedings of the 10th Annual International Symposium on Geoscience and Remote Sensing (IGARSS), College Park, MD, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lattari, L., Leno, B.G., Asaro, F., Rucci, A., Prati, C., and Matteucci, M. (2019). Deep learning for SAR image despeckling. Remote Sens., 11.","DOI":"10.3390\/rs11131532"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1109\/TGRS.2003.817844","article-title":"Almost translation invariant wavelet transformations for speckle reduction of SAR images","volume":"41","author":"Sveinsson","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"1953","article-title":"Deniosing by soft-thresholding","volume":"41","author":"Donoho","year":"1995","journal-title":"IEEE Trans. Inf. Theroy"},{"key":"ref_13","unstructured":"Guo, H., Odegard, J.E., and Lang, M. (1994, January 13\u201316). Wavelet based speckle reduction with application to SAR based ATD\/R. Proceedings of the International Conference on Image Processing(ICIP), Austin, TX, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2002.802473","article-title":"SAR speckle reduction using wavelet denoising and Markov random field modeling","volume":"40","author":"Xie","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TGRS.2003.813488","article-title":"SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling","volume":"41","author":"Achim","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/TGRS.2002.805083","article-title":"Speckle removal from SAR images in the undecimated wavelet domain","volume":"40","author":"Argenti","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1109\/TGRS.2012.2211366","article-title":"Bayesian wavelet shrinkage with heterogeneity-adaptive threshold for SAR image despeckling based on generalized gamma distribution","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1049\/el.2010.2982","article-title":"Improved bilateral filtering for SAR image despeckling","volume":"47","author":"Zhang","year":"2011","journal-title":"Electron. Lett."},{"key":"ref_19","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2005, January 20\u201326). A non-local algorithm for image denoising. Proceedings of the Computer Vision and Pattern Recognition (ICCVPR), San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/TGRS.2014.2352555","article-title":"NLSAR: A unified nonlocal framework for resolution-preserving (Pol)(In) SAR denoising","volume":"53","author":"Deledalle","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoising by sparse 3-D transform-domain collaborative filtering","volume":"16","author":"Kostadin","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TGRS.2011.2161586","article-title":"A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage","volume":"50","author":"Parrilli","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rudin, L., Lions, P.L., and Osher, S. (2003). Multiplicative Denoising and Deblurring: Theory and Algorithms. Geometric Level Set Methods in Imaging, Vision, and Graphics, Springer.","DOI":"10.1007\/0-387-21810-6_6"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10851-009-0180-z","article-title":"Multiplicative noise removal using L1 fidelity on frame coefficients","volume":"36","author":"Durand","year":"2010","journal-title":"J. Math. Imaging Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tabti, S., Deledalle, C.A., Denis, L., and Tupin, F. (2014, January 27\u201330). Modeling the distribution of patches with shift-invariance: Application to SAR image restoration. Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025018"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"347","DOI":"10.2528\/PIER13041503","article-title":"Structure preserveing SAR image despeckling via L0-minimazation","volume":"141","author":"Liu","year":"2013","journal-title":"Prog. Electromagn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5620","DOI":"10.3390\/rs5115620","article-title":"Bilateral distance based filtering for polarimatic SAR data","volume":"5","author":"Salembier","year":"2013","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xing, X.L., Chen, Q.H., Yang, S., and Liu, X.G. (2017). Feature-based nonlocal polarimetric SAR filtering. Remote Sens., 9.","DOI":"10.3390\/rs9101043"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1109\/TIP.2017.2713946","article-title":"MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?","volume":"26","author":"Deledalle","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","article-title":"FFDNet: Toward a fast and flexible solution for CNN-based image denoising","volume":"27","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chierchia, G., Cozzolino, D., Poggi, G., and Verdoliva, L. (2017, January 23\u201328). SAR image despeckling through convolutional neural networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128234"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1109\/LSP.2017.2758203","article-title":"SAR image despecking using a convolutional neural network","volume":"24","author":"Wang","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2018.2830100","article-title":"Learning a Dilated Residual Network for SAR Image Despeckling","volume":"10","author":"Zhang","year":"2018","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network","volume":"57","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, X.L., Denis, L., Tupin, F., and Yang, W. (2019, January 22\u201324). SAR image Depeckling using Pre-trained Convolutional Netural Network Models. Proceedings of the Joint Urban Remote Sensing Event (JURSE), Vannes, France.","DOI":"10.1109\/JURSE.2019.8809023"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1137\/15M1012682","article-title":"Estimation of the noise level function based on a nonparametric detection of homogeneous image regions","volume":"8","author":"Sutour","year":"2015","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_39","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the International Conference on Neural Information Processing Systems(NIPS), Lake Tahoe, NV, USA."},{"key":"ref_40","unstructured":"Sergey, I., and Christian, S. (2015, January 6\u201312). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1093\/biomet\/30.1-2.81","article-title":"A measure of rank correlation","volume":"30","author":"Kendall","year":"1938","journal-title":"Biometrika"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1093\/biomet\/33.3.239","article-title":"The treatment of ties in ranking problems","volume":"33","author":"Kendall","year":"1945","journal-title":"Biometrika"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TGRS.2002.1000333","article-title":"Statistical properties of logarithmically transformed speckle","volume":"40","author":"Xie","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1137\/070689954","article-title":"A nonlinear inverse scale space method for a convex multiplicative noise model","volume":"1","author":"Shi","year":"2008","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/s10851-009-0179-5","article-title":"Removing multiplicative noise by Douglas-Rachford splitting methods","volume":"36","author":"Steidl","year":"2010","journal-title":"J. Math. Imaging Vis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/TCI.2016.2629286","article-title":"Plug-and-play ADMM for image restoration: Fixed-point convergence and applications","volume":"3","author":"Chan","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_47","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2004, January 7\u201310). Multiscale structural similarity for image quality assessment. Proceedings of the Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3390\/rs9040389","article-title":"Unassisted Quantitative Evaluation of Despeckling Filters","volume":"9","author":"Luis","year":"2017","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TGRS.2011.2107915","article-title":"SAR image despeckling based on local homogeneous-region segmentation by using pixel-relativity measurement","volume":"49","author":"Feng","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yin, H., Gong, Y.H., and Qiu, G.P. (2019, January 16\u201320). Side Window Filtering. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00896"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2379\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:03Z","timestamp":1760189163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,14]]},"references-count":51,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11202379"],"URL":"https:\/\/doi.org\/10.3390\/rs11202379","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,14]]}}}