Ioffe S and Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 770–778, He K, Zhang X, Ren S and Sun J (2016) Deep residual learning for image recognition. Goodman JW (1976) Some fundamental properties of speckle. IEEE Conf Comput Vis Pattern Recognit, 2414–2423 Gatys LA, Ecker AS, Bethge M (2016) Image style transfer usingconvolutional neural networks. IEEE Trans Pattern Anal Mach Intell PAMI-4:157–166 įrost VS, Stiles JA, Shanmugan KS, Holtzman JC (1982) A model for radar images and its applicationto adaptive digital filtering of multiplicative noise. įerretti A, Fumagalli A, Novali F, Prati C, Rocca F, Rucci A (2011) A new algorithm for processing interferometric data-stacks: squeeSAR. IEEE Trans Geosci Remote Sens 50:2001–2025įeng W, Lei H, Gao Y (2014) Speckle reduction via higher order total variation approach. IEEE Access 6:21075–21083Įspinoza Molina D, Gleich D, Datcu M (2012) Evaluation of Bayesian despeckling and texture extraction methods based on Gauss–Markov and auto-binomial Gibbs random fields: application to TerraSAR-XData. Springer, 184– 199Įl-Latif AA, Abd-El-Atty B, Hossain MS, Rahman MA, Alamri A, Gupta BB (2018) Efficient quantum information hiding for remote medical image sharing. ĭong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution, In Proc Eur Conf Comput Vis. ĭeledalle C, Denis L, Tupin F (2009) Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. ĭabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by Sparse 3-D transform-domain collaborative filtering. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 606414. arXiv: 1704.00275ĭabov K, Foi A, Katkovnik V, Egiazarian K (2006) Image denoising with block-matching and 3D filtering", Proc. Ĭhierchia G, Cozzolino D, Poggi G and Verdoliva L (2017b) SAR image despeckling through convolutional neural networks. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, 20–25 2: 60–65Ĭhierchia G, El Gheche M, Scarpa G, Verdoliva L (2017a) Multitemporal SAR image despeckling based on block-matching and collaborative filtering. īuades A, Coll B, Morel J (2005) A non-local algorithm for image denoising.
Inverse Probl 14:R1–R54īioucas-Dias JM, Figueiredo MAT (2010) Multiplicative noise removal using variable splitting and constrained optimization. arXiv, arXiv:1511.00561īamler R, Hartl P (1998) Synthetic aperture radar interferometry. IEEE Geosci Remote Sens Mag 1(3):6–35īadrinarayanan V, Kendall A, Cipolla R (2015) SegNet: A deep convolutional encoder-decoder architecture for image segmentation. Argenti F, Lapini A, Bianchi T, Alparone L (2013) A tutorial on speckle reduction in synthetic aperture radar images.
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