A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Estimation of Adaptive Parameters for Satellite Image Deconvolution
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Improved adaptive wavelet threshold for image denoising
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Wavelet-based image denoising using a Markov random field a priori model
IEEE Transactions on Image Processing
Contourlet-based image fusion using information measures
WAV'08 Proceedings of the 2nd WSEAS International Conference on Wavelets Theory and Applications in Applied Mathematics, Signal Processing and Modern Science
IEEE Transactions on Signal Processing
Sparse representation based iterative incremental image deblurring
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hi-index | 0.00 |
The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem. Direct inversion leads to unacceptable noise amplification. Usually the problem is regularized during the inversion process. Recently, new approaches have been proposed, in which a rough deconvolution is followed by noise filtering in the wavelet transform domain. Herein, we have developed this second solution, by thresholding the coefficients of a new complex wavelet packet transform; all the parameters are automatically estimated. The use of complex wavelet packets enables translational invariance and improves directional selectivity, while remaining of complexity O(N). A new hybrid thresholding technique leads to high quality results, which exhibit both correctly restored textures and a high SNR in homogeneous areas. Compared to previous algorithms, the proposed method is faster, rotationally invariant and better takes into account the directions of the details and textures of the image, improving restoration. The images deconvolved in this way can be used as they are (the restoration step proposed here can be inserted directly in the acquisition chain), and they can also provide a starting point for an adaptive regularization method, enabling one to obtain sharper edges.