The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new type of Neural Shrinkage (NS) is presented with a new class of shrinkage architecture for speckle reduction in Synthetic Aperture Radar (SAR) images. The numerical results indicate that the new method outperforms the standard filters, the standard wavelet shrinkage despeckling method, and previous NS.
@article{arxiv.1608.00279,
title = {Neural shrinkage for wavelet-based SAR despeckling},
author = {Mario Mastriani and Alberto E. Giraldez},
journal= {arXiv preprint arXiv:1608.00279},
year = {2016}
}
Comments
12 pages, 7 figures, 2 tables. arXiv admin note: text overlap with arXiv:1607.03105, arXiv:1608.00273, arXiv:1608.00270, arXiv:1608.00277