{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:47:27Z","timestamp":1772725647363,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ant Group","award":["1504-250071690"],"award-info":[{"award-number":["1504-250071690"]}]},{"name":"Wuhan University","award":["1504-250071690"],"award-info":[{"award-number":["1504-250071690"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The speckle noise inherent in synthetic aperture radar (SAR) imaging has long posed a challenge for SAR data processing, significantly affecting image interpretation and recognition. Recently, deep learning-based SAR speckle removal algorithms have shown promising results. However, most existing algorithms rely on convolutional neural networks (CNN), which may struggle to effectively capture global image information and lead to texture loss. Besides, due to the different characteristics of optical images and synthetic aperture radar (SAR) images, the results of training with simulated SAR data may bring instability to the real-world SAR data denoising. To address these limitations, we propose an innovative approach that integrates swin transformer blocks into the prediction noise network of the denoising diffusion probabilistic model (DDPM). By harnessing DDPM\u2019s robust generative capabilities and the Swin Transformer\u2019s proficiency in extracting global features, our approach aims to suppress speckle while preserving image details and enhancing authenticity. Additionally, we employ a post-processing strategy known as pixel-shuffle down-sampling (PD) refinement to mitigate the adverse effects of training data and the training process, which rely on spatially uncorrelated noise, thereby improving its adaptability to real-world SAR image despeckling scenarios. We conducted experiments using both simulated SAR image datasets and real SAR image datasets, evaluating our algorithm from subjective and objective perspectives. The visual results demonstrate significant improvements in noise suppression and image detail restoration. The objective results demonstrate that our method obtains state-of-the-art performance, which outperforms the second-best method by an average peak signal-to-noise ratio (PSNR) of 0.93 dB and Structural Similarity Index (SSIM) of 0.03, affirming the effectiveness of our approach.<\/jats:p>","DOI":"10.3390\/rs16173222","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"3222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["SAR Image Despeckling Based on Denoising Diffusion Probabilistic Model and Swin Transformer"],"prefix":"10.3390","volume":"16","author":[{"given":"Yucheng","family":"Pan","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Liheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"Ant Group (Beijing), Beijing 100020, China"}]},{"given":"Jingdong","family":"Chen","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou 310020, China"}]},{"given":"Heping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Xianlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7513-6533","authenticated-orcid":false,"given":"Bin","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Hubei Luojia Laboratory, Wuhan 430075, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"ref_1","unstructured":"Moreira, J. (1990, January 20\u201324). Improved Multi Look Techniques Applied to SAR and Scan SAR Imagery. Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS 90, College Park, MD, USA."},{"key":"ref_2","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":"PAMI-2","author":"Lee","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","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_4","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_5","unstructured":"Lopes, 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 International Geoscience & Remote Sensing Symposium, College Park, MD, USA."},{"key":"ref_6","unstructured":"Guo, H., Odegard, J.E., Lang, M., Gopinath, R.A., Selesnick, I.W., and Burrus, C.S. (1994, January 13\u201316). Wavelet based speckle reduction with application to SAR based ATD\/R. Proceedings of the Image Processing, Austin, TX, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1364\/JOSAA.12.000686","article-title":"Iterative homomorphic technique for speckle reduction in synthetic-aperture radar imaging","volume":"12","author":"Franceschetti","year":"1995","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_8","unstructured":"Gagnon, L., and Jouan, A. (August, January 27). Speckle filtering of SAR images: A comparative study between complex-wavelet-based and standard filters. Proceedings of the Optical Science, Engineering and Instrumentation \u201997, San Diego, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/83.862630","article-title":"Spatially adaptive wavelet thresholding with context modeling for image denoising","volume":"9","author":"Chang","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1109\/TIP.2009.2029593","article-title":"Iterative weighted maximum likelihood denoising with probabilistic patch-based weights","volume":"18","author":"Deledalle","year":"2009","journal-title":"IEEE Trans Image Process"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/TGRS.2014.2352555","article-title":"NL-SAR: A unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising","volume":"53","author":"Deledalle","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1109\/LGRS.2013.2271650","article-title":"Fast Adaptive Nonlocal SAR Despeckling","volume":"11","author":"Cozzolino","year":"2013","journal-title":"IEEE Geoence Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","article-title":"Deep Learning on Image Denoising: An overview","volume":"131","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1109\/LSP.2017.2758203","article-title":"SAR Image Despeckling Using a Convolutional Neural Network","volume":"24","author":"Wang","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9336","DOI":"10.1109\/TGRS.2020.3034852","article-title":"Multi-Objective CNN-Based Algorithm for SAR Despeckling","volume":"59","author":"Vitale","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yuan, Q.Q., Li, J., Yang, Z., and Ma, X.S. (2018). Learning a Dilated Residual Network for SAR Image Despeckling. Remote Sens., 10.","DOI":"10.3390\/rs10020196"},{"key":"ref_21","unstructured":"Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Perera, M.V., Bandara, W.G.C., Valanarasu, J.M.J., and Patel, V.M. (2022, January 17\u201322). Transformer-Based Sar Image Despeckling. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884596"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JSTARS.2021.3132027","article-title":"SAR Image Despeckling Using Continuous Attention Module","volume":"15","author":"Ko","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, H., and Patel, V.M. (2017, January 10\u201313). Generative adversarial network-based restoration of speckled SAR images. Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cura\u00e7ao, The Netherlands.","DOI":"10.1109\/CAMSAP.2017.8313133"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gu, F., Zhang, H., and Wang, C. (2019, January 5\u20136). A GAN-based Method for SAR Image Despeckling. Proceedings of the SAR in Big Data Era: Models, Methods and Applications, Beijing, China.","DOI":"10.1109\/BIGSARDATA.2019.8858487"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, R., Li, Y., and Jiao, L. (2020, January 19\u201324). SAR Image Specle Reduction based on a Generative Adversarial Network. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206847"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4005305","DOI":"10.1109\/LGRS.2023.3270799","article-title":"SAR Despeckling Using a Denoising Diffusion Probabilistic Model","volume":"20","author":"Perera","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1109\/JSTARS.2021.3071864","article-title":"SAR2SAR: A semi-supervised despeckling algorithm for SAR images","volume":"14","author":"Dalsasso","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8807","DOI":"10.1109\/TGRS.2020.2990978","article-title":"SAR Image Despeckling by Noisy Reference-Based Deep Learning Method","volume":"58","author":"Ma","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"4704713","article-title":"As if by magic: Self-supervised training of deep despeckling networks with MERLIN","volume":"60","author":"Dalsasso","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"5204017","article-title":"Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks","volume":"60","author":"Molini","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TGRS.2013.2246794","article-title":"Separated Component-Based Restoration of Speckled SAR Images","volume":"52","author":"Patel","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2012). On Single Image Scale-Up Using Sparse-Representations. Curves and Surfaces, Springer.","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/TIP.2007.891788","article-title":"Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images","volume":"16","author":"Foi","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"023016","DOI":"10.1117\/1.3600632","article-title":"Color demosaicking by local directional interpolation and nonlocal adaptive thresholding","volume":"20","author":"Zhang","year":"2011","journal-title":"J. Electron. Imaging"},{"key":"ref_39","unstructured":"Franzen, R. (2024, July 18). Kodak Lossless True Color Image Suite. Available online: https:\/\/www.scirp.org\/reference\/referencespapers?referenceid=727938."},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Perera, M.V., Bandara, W.G.C., Valanarasu, J.M.J., and Patel, V.M. (2022, January 17\u201322). SAR Despeckling Using Overcomplete Convolutional Networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884632"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/JSTARS.2013.2279501","article-title":"Quality Assessment of Despeckled SAR Images","volume":"7","author":"Dellepiane","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1109\/TGRS.2013.2252907","article-title":"Benchmarking Framework for SAR Despeckling","volume":"52","author":"Poderico","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1109\/TGRS.2016.2517627","article-title":"SAR Image Despeckling by the Use of Variational Methods With Adaptive Nonlocal Functionals","volume":"54","author":"Ma","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gomez, L., Ospina, R., and Frery, A.C. (2017). Unassisted Quantitative Evaluation of Despeckling Filters. Remote Sens., 9.","DOI":"10.3390\/rs9040389"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_49","unstructured":"Dhariwal, P., and Nichol, A. (2021, January 6\u201314). Diffusion Models Beat GANs on Image Synthesis. Proceedings of the NIPS\u201921: Proceedings of the 35th International Conference on Neural Information Processing Systems, Virtual."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3222\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:02Z","timestamp":1760111162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":49,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173222"],"URL":"https:\/\/doi.org\/10.3390\/rs16173222","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]}}}