In this work, we propose a novel prior term for the regularization of blind deblurring methods. The proposed method introduces machine learning techniques into the blind deconvolution process. The proposed technique has sound mathematical foundations and is generic to many inverse problems. We demonstrate the usage of this regularizer within Bayesian blind deconvolution framework, and also inte...

Inverse problems appear in many applications such as image deblurring and inpainting. The common approach to address them is to design a specific algorithm for each problem. The Plug-and-Play (P&P) framework, which has been recently introduced, allows solving general inverse problems by leveraging the impressive capabilities of existing denoising algorithms. While this fresh strategy has found ...

The wavelet frame systems have been playing an active role in image restoration and many other image processing fields over the past decades, owing to the good capability of sparsely approximating piece-wise smooth functions such as images. In this paper, we propose a novel wavelet frame based sparse recovery model called Support Driven Sparse Regularization (SDSR) for image deblurring, where t...

Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to replace traditional MAP. We develop an image reconstruction a...

Motivated by the hierarchical multiscale image representation of Tadmor et al., we propose a novel integrodifferential equation (IDE) for a multiscale image representation. To this end, one integrates in inverse scale space a succession of refined, recursive ‘slices’ of the image, which are balanced by a typical curvature term at the finer scale. Although the original motivation came from a var...

In this paper, we study the problem of recovering a sharp version of a given blurry image when the blur kernel is unknown. Previous methods often introduce an image-independent regularizer (such as Gaussian or sparse priors) on the desired blur kernel. We shall show that the blurry image itself encodes rich information about the blur kernel. Such information can be found through analyzing and c...

Motion deblurring is a long standing problem in computer vision and image processing. In most previous approaches, the blurred image is modeled as the convolution of a latent intensity image with a blur kernel. However, for images captured by a real camera, the blur convolution should be applied to scene irradiance instead of image intensity and the blurred results need to be mapped back to ima...

Depth from defocus and defocus deblurring from a single image are two challenging problems that are derived from the finite depth of field in conventional cameras. Coded aperture imaging is one of the techniques that is used for improving the results of these two problems. Up to now, different methods have been proposed for improving the results of either defocus deblurring or depth estimation....

In this paper, we propose a fast primal-dual algorithm for solving bilaterally constrained total variation minimization problems which subsume the bilaterally constrained total variation image deblurring model and the two-phase piecewise constant Mumford-Shah image segmentation model. The presence of the bilateral constraints makes the optimality conditions of the primal-dual problem semi-smoot...

Abstract Blur is an image degradation that makes object recognition challenging. Restoration approaches solve this problem via deblurring, deep learning methods rely on the augmentation of training sets. Invariants with respect to blur offer alternative way describing and recognising blurred images without any deblurring data augmentation. In paper, we present original theory invariants. Unlike...