In previous studies, we developed a blind deblurring algorithm based on the edge-to-noise ratio (ENR) and EM algorithm to improve the quality of spiral CT images. In this work, we apply the Wiener filter in place of the EM algorithm to improve the timing performance. The algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter feature...

Image motion deblurring with unknown blur kernel is an ill-posed problem. This paper proposes a blind motion deblurring approach that solves blur kernel and the latent image robustly. For kernel optimization, an edge mask is used as an image prior to improve kernel update, then an edge selection mask is adopted to improve image update. In addition, an alternative iterative method is introduced ...

Alternating methods for image deblurring and denoising have recently received considerable attention. The simplest of these methods are two-way methods that restore contaminated images by alternating between deblurring and denoising. This paper describes Krylov subspace-based two-way alternating iterative methods that allow the application of regularization operators different from the identity...

We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input i...

To recover a sharp image from its blurry observation is the problem known as image deblurring. It frequently arises in imaging sciences and technologies, including optical, medical, and astronomical applications, and is a crucial step towards successful detections of important patterns such as abnormal tissues and surface details of a distant planet. Mathematically, image deblurring is intimate...

This paper presents a novel blind motion deblurring algorithm for dealing with non-uniform motion blurs caused by camera shakes. Conventional motion deblurring algorithms assume the blur can be described by a uniform point spread function (PSF). However, this assumption is typically invalid in practice. We exploit a new representation of motion blurs which models the blur effects of camera shak...

To recover a sharp image from its blurry observation is the problem known as image deblurring. It frequently arises in imaging sciences and technologies, including optical, medical, and astronomical applications, and is crucial for allowing to detect important features and patterns such as those of a distant planet or some microscopic tissue. Mathematically, image deblurring is intimately conne...

Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, deconvolve blurry input to restore the target image. In this paper, we propose a graph-based blind image deblurring algorithm by interpreting an image patch as a...