In this paper, we propose a novel second-order regularizer based on the maximum response of the second-order directional derivative, assuming that the image under consideration belongs to the class of piecewise-linear signals. Compared to total-variation regularization that preserves edges but transforms piecewise-smooth regions into piecewise-constant regions, the proposed model is able to restore piecewise-linear regions and finer details. Deconvolution experiments demonstrate the performance of our approach in terms of the quality of reconstruction
We propose an adaptive norm strategy designed for the restora- tion of images contaminated by blur ...
We propose an adaptive norm strategy designed for the re-storation of images contaminated by blur an...
Abstract — This paper presents a new approach to image decon-volution (deblurring), under total vari...
The total variation regularizer is well suited to piecewise smooth images. If we add the fact that t...
We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order ex...
The total variation (TV) regularization method is an effective method for image deblurring in preser...
Abstract. Total variation (TV) regularization, originally introduced by Rudin, Osher and Fatemi in t...
In order to restore the high quality image, we propose a compound regularization method which combin...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
International audienceTo resolve the image deconvolution problem, thetotal variation (TV) minimizati...
none4noWe propose two new variational models aimed to outperform the popular total variation (TV) mo...
Abstract. In many inverse problems it is essential to use regularization methods that preserve edges...
Abstract: Image deblurring is a challenging illposed problem with widespread applications. Most exis...
This paper proposes an extension of total variation (TV) im-age deconvolution technique that enhance...
We introduce a new general TV regularizer, namely, generalized TV regularization, to study image den...
We propose an adaptive norm strategy designed for the restora- tion of images contaminated by blur ...
We propose an adaptive norm strategy designed for the re-storation of images contaminated by blur an...
Abstract — This paper presents a new approach to image decon-volution (deblurring), under total vari...
The total variation regularizer is well suited to piecewise smooth images. If we add the fact that t...
We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order ex...
The total variation (TV) regularization method is an effective method for image deblurring in preser...
Abstract. Total variation (TV) regularization, originally introduced by Rudin, Osher and Fatemi in t...
In order to restore the high quality image, we propose a compound regularization method which combin...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
International audienceTo resolve the image deconvolution problem, thetotal variation (TV) minimizati...
none4noWe propose two new variational models aimed to outperform the popular total variation (TV) mo...
Abstract. In many inverse problems it is essential to use regularization methods that preserve edges...
Abstract: Image deblurring is a challenging illposed problem with widespread applications. Most exis...
This paper proposes an extension of total variation (TV) im-age deconvolution technique that enhance...
We introduce a new general TV regularizer, namely, generalized TV regularization, to study image den...
We propose an adaptive norm strategy designed for the restora- tion of images contaminated by blur ...
We propose an adaptive norm strategy designed for the re-storation of images contaminated by blur an...
Abstract — This paper presents a new approach to image decon-volution (deblurring), under total vari...