Optimal estimation and sequential channel equalization algorithms for Chaotic communications systems
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
Digital Image Processing
Digital Image Restoration
Recursive Model-Based Image Restoration
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
The PSF Correction Method for Satellite Image Restoration
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Blur identification using averaged spectra of degraded image singular vectors
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Instantaneous spectrum estimation of event-based densities
EURASIP Journal on Applied Signal Processing
A general formulation for iterative restoration methods
IEEE Transactions on Signal Processing
A regularization approach to joint blur identification and image restoration
IEEE Transactions on Image Processing
Iterative image restoration using approximate inverse preconditioning
IEEE Transactions on Image Processing
Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing
Adaptively regularized constrained total least-squares image restoration
IEEE Transactions on Image Processing
Blind identification of multichannel FIR blurs and perfect image restoration
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
IEEE Transactions on Image Processing
A robust nonlinear filter for image restoration
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
Blurred image restoration: A fast method of finding the motion length and angle
Digital Signal Processing
An MLP neural net with L1 and L2 regularizers for real conditions of deblurring
EURASIP Journal on Advances in Signal Processing
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The restoration achieved on the basis of a Wiener scheme is an optimum since the restoration filter is the outcome of a minimisation process. Moreover, the Wiener restoration approach requires the estimation of some parameters related to the original image and the noise, as well as knowledge about the PSF function. However, in a real restoration problem, we may not possess accurate values of these parameters, making results relatively far from the desired optimum. Indeed, a desensitisation process is required to decrease this dependency on the parameter errors of the restoration filter. In this paper, we present an iterative method to reduce the sensitivity of a general restoration scheme (but specified to the Wiener filter) with regards to wrong estimates of the said parameters. Within the Fourier transform domain, a sensitivity analysis is tackled in depth with the purpose of defining a number of iterations for each frequency element, which leads to the aimed desensitisation regardless of the errors on estimates. Experimental computations using meaningful values of parameters are addressed. The proposed technique effectively achieves better results than those obtained when using the same wrong estimates in the Wiener approach, as well as verified on an SAR restoration.