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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 32 — Nov. 10, 2010
  • pp: 6286–6294

Satellite image deconvolution based on nonlocal means

Ming Zhao, Wei Zhang, Zhile Wang, and Qingyu Hou  »View Author Affiliations


Applied Optics, Vol. 49, Issue 32, pp. 6286-6294 (2010)
http://dx.doi.org/10.1364/AO.49.006286


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Abstract

The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized under the Bayesian framework by introducing an appropriate image prior. In this paper, we derive a new image prior based on the state-of-the-art nonlocal means (NLM) denoising approach under Markov random field theory. Inheriting from the NLM, the prior exploits the intrinsic high redundancy of satellite images and is able to encode the image’s nonsmooth information. Using this prior, we propose an inhomogeneous deconvolution technique for satellite images, termed nonlocal means-based deconvolution (NLM-D). Moreover, in order to make our NLM-D unsupervised, we apply the L-curve approach to estimate the optimal regularization parameter. Experimentally, NLM-D demonstrates its capacity to preserve the image’s nonsmooth structures (such as edges and textures) and outperforms the existing total variation-based and wavelet-based deconvolution methods in terms of both visual quality and signal-to-noise ratio performance.

© 2010 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.1830) Image processing : Deconvolution
(100.3010) Image processing : Image reconstruction techniques
(100.3020) Image processing : Image reconstruction-restoration

ToC Category:
Image Processing

History
Original Manuscript: May 18, 2010
Revised Manuscript: September 23, 2010
Manuscript Accepted: September 24, 2010
Published: November 5, 2010

Citation
Ming Zhao, Wei Zhang, Zhile Wang, and Qingyu Hou, "Satellite image deconvolution based on nonlocal means," Appl. Opt. 49, 6286-6294 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-32-6286


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