An adaptive Gaussian model for satellite image deblurring

• Published in: IEEE transactions on image processing Vol. 13; no. 4; pp. 613 - 621
• Main Authors: Jalobeanu, A., Blanc-Feraud, L., Zerubia, J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-04-2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Approximation; Artificial Intelligence; Bayesian methods; Computer Simulation; Context modeling; Deconvolution; Detection, estimation, filtering, equalization, prediction; Economic models; Environmental Monitoring - methods; Estimates; Exact sciences and technology; Feedback; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image reconstruction; Image restoration; Information Storage and Retrieval - methods; Information, signal and communications theory; Inverse problems; Mathematical analysis; Mathematical models; Maximum likelihood estimation; Models, Statistical; Noise robustness; Normal Distribution; Parameter estimation; Pattern Recognition, Automated; Reproducibility of Results; Satellites; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Spacecraft; Studies; Telecommunications and information theory; Texture; Parameter estimation; High resolution; Image processing; Noise reduction; Signal estimation; Satellite observation; inhomogeneous Gaussian models; Image restoration; Algorithm; Blurred image; Adaptive method; Texture; Noisy image; Deconvolution; Gaussian process; Wavelet transformation; Deconvolution,estimation techniques; A priori estimation; Signal processing; Ill posed problem; Maximum likelihood; Signal to noise ratio; satellite images
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2003.819969

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.