Blind Image Deblurring Using Row–Column Sparse Representations

Blind Image Deblurring Using Row-Column Sparse Representations

Blind image deblurring is a particularly challenging inverse problem where the blur kernel is unknown and must be estimated en route to recover the deblurred image. The problem is of strong practical relevance since many imaging devices such as cellphone cameras, must rely on deblurring algorithms to yield satisfactory image quality. Despite significant research effort, handling large motions r...

Blind image deblurring via coupled sparse representation

The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learningbased method of estimating blur kernel under the ‘0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the b...

Blind Motion Deblurring Using Image Statistics

We address the problem of blind motion deblurring from a single image, caused by a few moving objects. In such situations only part of the image may be blurred, and the scene consists of layers blurred in different degrees. Most of of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image. However, in the case of different motions, the blu...

Image Denoising Using Sparse Representations

The problem of removing white zero-mean Gaussian noise from an image is an interesting inverse problem to be investigated in this paper through sparse and redundant representations. However, finding the sparsest possible solution in the noise scenario was of great debate among the researchers. In this paper we make use of new approach to solve this problem and show that it is comparable with th...

Blind image deblurring using elastic-net based rank prior