195
Views
0
CrossRef citations to date
0
Altmetric
Research Article

SAR image despeckling using a CNN guided by high-frequency information

, , , &
Pages 441-451 | Received 25 Apr 2022, Accepted 04 Nov 2022, Published online: 15 Nov 2022

References

  • Gleich D, Datcu M. Wavelet-based SAR image despeckling and information extraction, using particle filter. IEEE Trans Image Process. 2009;18(10):2167–2184.
  • Raney RK. SAR processing of partially coherent phenomena. Int J Remote Sens. 1980;1(1):29–51.
  • Miao S, Liu X. Joint sparse representation of complementary components in SAR images for robust target recognition. J Electromagn Waves Appl. 2019;33(7):882–896.
  • Lee JS. Speckle suppression and analysis for synthetic aperture radar images. Opt Photonics. 1985;25(5):170–179.
  • Baraldi A, Parmiggiani F. An alternative form of the Lee filter for speckle suppression in SAR images. Graph Model Image Process. 1995;57(1):75–78.
  • Fukuda S, Hirosawa H. Suppression of speckle in synthetic aperture radar images using wavelet. Int J Remote Sens. 1998;19(3):507–519.
  • Lord RT, Inggs MR. Efficient RFI suppression in SAR using LMS adaptive filter integrated with range/Doppler algorithm. Electron Lett. 1999;35(8):629–630.
  • Li C. Two adaptive filters for speckle reduction in SAR images by using the variance ratio. Int J Remote Sens. 1988;9(4):641–653.
  • Masayoshi T, Miki H, Hideo K. A study of Kalman filtering for noise reduction on SAR images. ITE Technical Report. 2000;24(11):1–6.
  • Kulpa K, Malanowski M, Misiurewicz J, et al. Radar and optical images fusion using stripmap SAR data with multilook processing. Int J Electron Telecommun. 2011;57(1):37–42.
  • Sethunadh R, Thomas T. SAR image despeckling using adaptive multiscale products thresholding in directionlet domain. Electron Lett. 2013;49(18):1183–1184.
  • Hazarika D, Nath VK, Bhuyan M. Speckle removal from SAR images in the lapped transform domain using adaptive threshold based on despeckling evaluation indexes. Procedia Comput Sci. 2016;87:148–155.
  • Zhang DX, Gao QW, Wu XP. Bayesian-Based speckle suppression for SAR image using contourlet transform. J Electron Sci Technol. 2008;6(1):79–82.
  • Shanthi I, Valarmathi ML. Comparative study of transform domain filters with modified particle swarm optimization for speckle noise suppression of SAR images. Int J Tomogr Simul. 2014;26(2):89–104.
  • Zhang WG, Zhang Q. SAR image despeckling combining target detection with improved nonlocal means. Electron Lett. 2011;47(12):724–725.
  • Torres L, Sant'Anna SJS, Freitas C, et al. Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means. Pattern Recognit. 2014;47(1).
  • Zhu HM, Zhong WQ, Jiao LC. Combination of target detection and block-matching 3D filter for despeckling SAR images. Electron Lett. 2013;49(7):495–496.
  • Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–1554.
  • Lattari F, Leon BG, Asaro F, et al. Deep learning for SAR image despeckling. Remote Sens. 2019;11(13):1532–1551.
  • Painam RK, Suchetha M. Despeckling of SAR images using BEMD-based adaptive frost filter. J Indian Soc Remote Sens. 2022: 1–12.
  • Liu S, Lei Y, Zhang L, et al. MRDDANet: a multiscale residual dense dual attention network for SAR image denoising. IEEE Trans Geosci Remote Sens. 2021;60:1–13.
  • Liu S, Gao L, Lei Y, et al. SAR speckle removal using hybrid frequency modulations. IEEE Trans Geosci Remote Sens. 2020;59(5):3956–3966.
  • Chierchia G, Cozzolino D, Poggi G, et al. SAR Image despeckling through convolutional neural networks. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS); 2017, p. 5438–5441.
  • Ming Z, Li DY, Da HY, et al. Synthetic aperture radar image despeckling with a residual learning of convolutional neural network. Optik. 2021;228(165):876–883.
  • Dheeren KM, Prosad RL. Efficient modelling of SAR texture with a gamma-inverse gamma distribution for MAP-based speckle suppression. IET Radar Sonar Navig. 2019;13(6):1018–1030.
  • Wang P, Zhang H, Patel VM. SAR image despeckling using a convolutional neural network. IEEE Signal Process Lett. 2017;24(12):1763–1767.
  • Ahn H, Yim C. Convolutional neural networks using skip connections with layer groups for super-resolution image reconstruction based on deep learning. Appl Sci. 2020;10(6):1956–1968.
  • Zhang Q, Yuan Q, Li J, et al. Learning a dilated residual network for SAR image despeckling. Remote Sens. 2018;10(2):196–213.
  • Gui Y, Xue L, Li X. SAR image despeckling using a dilated densely connected network. Remote Sens Lett. 2018;9(9):857–866.
  • Heinrich MP, Stille M, Buzug TM. Residual U-net convolutional neural network architecture for low-dose CT denoising. Curr Dir Biomed Eng. 2018;4(1):297–300.
  • Liu G, Kang H, Wang Q, et al. Contourlet-CNN for SAR image despeckling. Remote Sens. 2021;13(4):764–783.
  • Liu S, Liu T, Gao L, et al. Convolutional neural network and guided filtering for SAR image denoising. Remote Sens. 2019;11(6):702–720.
  • Cristovao C, Alessandro F, Vladimir K, et al. Nonlocality-reinforced convolutional neural networks for image denoising. IEEE Signal Process Lett. 2018;25(8):1216–1220.
  • Zifei Y, Shi G, Gang X, et al. On combining CNN with non-local self-similarity based image denoising methods. IEEE Access. 2020;8:14789–14797.
  • Liu P, Zhang H, Zhang K, et al. Multi-level wavelet-CNN for image restoration. CoRR. 2018; abs/1805.07071.
  • Kai Z, Wangmeng Z, Lei Z. FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Trans Image Process. 2018;27(9):4608–4622.
  • Ayachi RE, Gouskir M, Baslam M. Application of haar wavelets on medical images. J Electron Commer Organ. 2015;13(2):41–49.
  • Kumar M, Diwakar M. CT image denoising using locally adaptive shrinkage rule in tetrolet domain. J King Saud Univ Comput Inf Sci. 2018;30(1):41–50.
  • Cheng G, Han J, Lu X. Remote sensing image scene classification: benchmark and state of the art. Proc IEEE. 2017;105(10):1865–1883.
  • Deledalle CA, Denis L, Tupin F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process. 2009;18(12):2661–2672.
  • Ko J, Lee S. SAR image despeckling using continuous attention module. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;15:3–19.
  • Wang H, Ding Z, Li X, et al. Convolutional neural network with a learnable spatial activation function for SAR image despeckling and forest image analysis. Remote Sens. 2021;13(17):3444.
  • Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39(6):1137–1149.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.