Motion blur due to camera shake is one of the predominant sources of degradation in handheld photography. Single image blind deconvolution (BD) or motion deblurring aims at restoring a sharp latent image from the blurred recorded picture without knowing the camera motion that took place during the exposure. BD is a long-standing problem, but has attracted much attention recently, cumulating in ...

Observed images are usually blurred versions of the true images, due to imperfections of the imaging devices, atmospheric turbulence, out of focus lens, motion blurs, and so forth. The major purpose of image deblurring is to restore the original image from its blurred version. A blurred image can be described by the convolution of the original image with a point spread function (psf) that chara...

We address the problem of estimating a random vector X from two sets of measurements Y and Z, such that the estimator is linear in Y . We show that the partially linear minimum mean squared error (PLMMSE) estimator requires knowing only the second-order moments of X and Y , making it of potential interest in various applications. We demonstrate the utility of PLMMSE estimation in recovering a s...

Obtained images through imaging systems are considered as degraded versions of the original view. Computed Tomography (CT) images have different types of degradations such as noise, blur and contrast imperfections. This paper handles the issue of deblurring CT medical images affected by Gaussian blur. Image deblurring is the procedure of decreasing the blur amount and grant the filtered image w...

Computed Tomography (CT)-based Finite Element (FE) models of femur have been extensively validated and recently applied to clinical studies showing encouraging results [1]. Nevertheless, there remains room for improvement in the accuracy of these models. Validation of FE models against in-vitro strain measurements and failure tests have identified systematic errors at sites where the cortical s...

Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g., superresolution from a single image). In this paper we show how this multi-scale property can also be used for “blind-deblurring”, namely, removal of an unknown blur from a blurry image. While patches repeat ‘as is’ across scales in a sharp natural image, t...

We consider variations of the Rudin-Osher-Fatemi functional which are particularly well-suited to denoising and deblurring of 2D bar codes. These functionals consist of an anisotropic total variation favoring rectangles and a fidelity term which measure the L distance to the signal, both with and without the presence of a deconvolution operator. Based upon the existence of a certain associated ...

We present a two-stage method for obtaining both phase and object estimates from phase-diversity time series data. In the first stage, the phases are estimated for each time frame using the limited memory BFGS method. In the second stage, an algorithm that incorporates a nonnegativity constraint as well prior knowledge of data noise statistics is used to obtain an estimate of the object being o...

We present a novel joint nonuniform illumination estimation and deblurring method for bar code signals based on a penalized nonlinear squares objective function. The objective function is based on the proper parameterization of a bar code signal and nonuniform illumination as well as a regularization on the illumination using a smoothness penalty. By the minimization of the objective function, ...

We propose a variational approach for deblurring and impulsive noise removal in multi-channel images. A robust data fidelity measure and edge preserving regularization are employed. We consider several regularization approaches, such as Beltrami flow, Mumford-Shah and Total-Variation Mumford-Shah. The latter two methods are extended to multi-channel images and reformulated using the Γ -converge...