Both regularization and compression are important issues in image processing and have been widely approached in the literature. The usual procedure to obtain the compression of an image given through a noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the regularized image to get an approximation in terms of low rank nonnegative factors. We examine here the possibility of swapping the two steps by deblurring directly the noisy factors or partially denoised factors. The experimentation shows that in this way images with comparable regularized compression can be obtained with a lower computational cost
ABSTRACT: Image deblurring (ID) is an ill-posed problem typically addressed by using regularization,...
Image deconvolution is an important pre-processing step in image analysis which may be combined with...
In many real applications, traditional super-resolution (SR) methods fail to provide high-resolution...
Both regularization and compression are important issues in image processing and have been widely ap...
Both regularization and compression are important issues in image processing and have been widely ap...
We are interested in fast and stable iterative regularization methods for image deblurringproblems w...
Preconditioning techniques for linear systems are widely used in order to speed up the convergence o...
This Letter proposes a novel method to deblur a blurry image corrupted by noise. The authors estimat...
Thresholding iterative methods are recently successfully applied to image deblurring problems. In t...
Image deconvolution is an ill-posed problem that requires a regularization term to solve. The most c...
This paper proposes a two-phase fully automatic scheme for restoring images that have been corrupted...
This paper faces the problem of denoising compressed images, obtained through a quantization in a kn...
This paper presents a couple of preconditioning techniques that can be used to enhance the performan...
Sparsity based regularization methods for image restoration assume that the underlying image has a g...
In this paper, we propose the use of complexity regularization in image restoration. This is a flexi...
ABSTRACT: Image deblurring (ID) is an ill-posed problem typically addressed by using regularization,...
Image deconvolution is an important pre-processing step in image analysis which may be combined with...
In many real applications, traditional super-resolution (SR) methods fail to provide high-resolution...
Both regularization and compression are important issues in image processing and have been widely ap...
Both regularization and compression are important issues in image processing and have been widely ap...
We are interested in fast and stable iterative regularization methods for image deblurringproblems w...
Preconditioning techniques for linear systems are widely used in order to speed up the convergence o...
This Letter proposes a novel method to deblur a blurry image corrupted by noise. The authors estimat...
Thresholding iterative methods are recently successfully applied to image deblurring problems. In t...
Image deconvolution is an ill-posed problem that requires a regularization term to solve. The most c...
This paper proposes a two-phase fully automatic scheme for restoring images that have been corrupted...
This paper faces the problem of denoising compressed images, obtained through a quantization in a kn...
This paper presents a couple of preconditioning techniques that can be used to enhance the performan...
Sparsity based regularization methods for image restoration assume that the underlying image has a g...
In this paper, we propose the use of complexity regularization in image restoration. This is a flexi...
ABSTRACT: Image deblurring (ID) is an ill-posed problem typically addressed by using regularization,...
Image deconvolution is an important pre-processing step in image analysis which may be combined with...
In many real applications, traditional super-resolution (SR) methods fail to provide high-resolution...