We present an iterative deconvolution algorithm that minimizes a functional with a non-quadratic wavelet-domain regularization term. Our approach is to introduce subband-dependent parameters into the bound optimization framework of Daubechies et al.; it is sufficiently general to cover arbitrary choices of wavelet bases (non-orthonormal or redundant). The resulting procedure alternates between the following two steps
AbstractNonlinear thresholding of wavelet coefficients is an efficient method for denoising signals ...
High-resolution image reconstruction refers to reconstructing high-resolution images from multiple ...
Deconvolution, Quadratic functionals, Adaptive curve estimation, Wavelets, Global thresholding,
This paper presents new methods for computing the step sizes of the subband-adaptive iterative shrin...
We present a modified version of the deconvolution algorithm introduced by Figueiredo and Nowak, whi...
We present a modified version of the deconvolution algorithm introduced by Figueiredo and Nowak, whi...
Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to ...
International audienceIn this paper, we propose a data-driven block thresholding procedure for wavel...
International audienceIn this paper, we propose a data-driven block thresholding procedure for wavel...
Abstract — Standard formulations of image/signal deconvolution under wavelet-based priors/regularize...
In this paper, we propose a new approach to wavelet-based deconvolution. Roughly speaking, the algor...
In this paper, we propose a new approach to wavelet-based de-convolution. Roughly speaking, the algo...
Conference PaperWe propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-d...
Conference PaperIn this paper, we propose a new approach to wavelet-based deconvolution. Roughly spe...
We propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-domain system inv...
AbstractNonlinear thresholding of wavelet coefficients is an efficient method for denoising signals ...
High-resolution image reconstruction refers to reconstructing high-resolution images from multiple ...
Deconvolution, Quadratic functionals, Adaptive curve estimation, Wavelets, Global thresholding,
This paper presents new methods for computing the step sizes of the subband-adaptive iterative shrin...
We present a modified version of the deconvolution algorithm introduced by Figueiredo and Nowak, whi...
We present a modified version of the deconvolution algorithm introduced by Figueiredo and Nowak, whi...
Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to ...
International audienceIn this paper, we propose a data-driven block thresholding procedure for wavel...
International audienceIn this paper, we propose a data-driven block thresholding procedure for wavel...
Abstract — Standard formulations of image/signal deconvolution under wavelet-based priors/regularize...
In this paper, we propose a new approach to wavelet-based deconvolution. Roughly speaking, the algor...
In this paper, we propose a new approach to wavelet-based de-convolution. Roughly speaking, the algo...
Conference PaperWe propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-d...
Conference PaperIn this paper, we propose a new approach to wavelet-based deconvolution. Roughly spe...
We propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-domain system inv...
AbstractNonlinear thresholding of wavelet coefficients is an efficient method for denoising signals ...
High-resolution image reconstruction refers to reconstructing high-resolution images from multiple ...
Deconvolution, Quadratic functionals, Adaptive curve estimation, Wavelets, Global thresholding,