Ultrasound images speckle noise removal by nonconvex hybrid overlapping group sparsity model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-022-02627-7.pdf
Reference32 articles.
1. Afonso, M., Miguel, S.J.: Image reconstruction under multiplicative speckle noise using total variation. Neurocomputing 150, 200–213 (2015). https://doi.org/10.1016/j.neucom.2014.08.073
2. Hou, B., Zhang, X., Bu, X., Feng, H.: SAR image despeckling based on nonsubsampled shearlet transform. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 5(3), 809–823 (2012). https://doi.org/10.1109/JSTARS.2012.2196680
3. Parrilli, S., Poderico, M., Angelino, C.V., Verdoliva, L.: A nonlocal sar image denoising algorithm based on llmmse wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50(2), 606–616 (2012). https://doi.org/10.1109/TGRS.2011.2161586
4. R$$\rm {\ddot{o}}$$hlig, M., Schmidt, C., Prakasam, R.K., Schumann, H., Stachs, O.,: Visual analysis of retinal changes with optical coherence tomography. Vis. Comput. 34(1), 1209–1224 (2018). https://doi.org/10.1007/s00371-018-1486-x
5. Krissian, K., Kikinis, R., Westin, C.F. Vosburgh, K.G.: Speckle-Constrained Filtering of Ultrasound Images. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). 547–552 (2005). https://doi.org/10.1109/CVPR.2005.331
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