This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by low-resolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two-step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
@article{arxiv.1608.00274,
title = {Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images},
author = {Mario Mastriani},
journal= {arXiv preprint arXiv:1608.00274},
year = {2016}
}
Comments
10 pages, 7 figures, 2 tables. arXiv admin note: text overlap with arXiv:1608.00273; text overlap with arXiv:1002.3985 by other authors without attribution