2016/07/31 by Mario Mastriani, Mastriani, Mario, Alberto Giráldez +2 · 4 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Fault Diagnosis Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1608.00279
12 pages, 7 figures, 2 tables. arXiv admin note: text overlap with arXiv:1607.03105, arXiv:1608.00273, arXiv:1608.00270, arXiv:1608.00277
arxiv created 2016/07/31 · openalex publication_date 2016/07/31 · arxiv updated 2016/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.