PMID: 15376594Sep 21, 2004Paper

An adaptive Gaussian model for satellite image deblurring

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
André JalobeanuJosiane Zerubia

Abstract

The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized within a Bayesian context by using an a priori model of the reconstructed solution. Since real satellite data show spatially variant characteristics, we propose here to use an inhomogeneous model. We use the maximum likelihood estimator (MLE) to estimate its parameters and we show that the MLE computed on the corrupted image is not suitable for image deconvolution because it is not robust to noise. We then show that the estimation is correct only if it is made from the original image. Since this image is unknown, we need to compute an approximation of sufficiently good quality to provide useful estimation results. Such an approximation is provided by a wavelet-based deconvolution algorithm. Thus, a hybrid method is first used to estimate the space-variant parameters from this image and then to compute the regularized solution. The obtained results on high resolution satellite images simultaneously exhibit sharp edges, correctly restored textures, and a high SNR in homogeneous areas, since the proposed technique adapts to the local characteristics of the data.

References

Feb 13, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·X DescombesM Berthod
Jan 1, 1997·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·P CharbonnierM Barlaud
Jun 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Geman, D Geman

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Citations

May 17, 2005·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Daan ZhuRichard Lee
Nov 13, 2014·Medical & Biological Engineering & Computing·Gorkem SerbesNizamettin Aydin
May 12, 2007·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Erik F Y HomJohn W Sedat
Nov 12, 2010·Applied Optics·Ming ZhaoQingyu Hou
Aug 22, 2009·Applied Optics·Wei ZhangZhile Wang
Sep 24, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yuan-Kai Wang, Ching-Tang Fan
Oct 15, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Francesco ConteGiulio Iannello
Apr 14, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Levente Kovács, Tamás Szirányi
Jan 22, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Feng XueThierry Blu
Jan 29, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Prakash P Gajjar, Manjunath V Joshi
Jul 13, 2006·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Max Mignotte

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