In various learning-based image restoration tasks, such as denoising and super-resolution, the degradation representations were widely used to model process handle complicated patterns. However, they are less explored in deblurring blur kernel estimation cannot perform well real-world challenging cases. We argue that it is particularly necessary for since blurry patterns typically show much lar...

Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local object movements. To fill the vacancy of in real scenes, we establish first motion dataset (ReLoBlur), which is captured a synchronized beam-splitting photographing system and corrected post-progressing pipeline. Based ReLoBlur, propose Local Blur-Aware Gated network (LBAG)...

Article history: Received 19 June 2015 Accepted 16 May 2016 Available online xxxx

Regularization operators that penalize derivatives are approximated by Kronecker products. This allows direct regularization of multidimensional first-kind linear integral equations with separable kernels. An example of image deblurring is presented.

When we use a camera, we want the recorded image to be a faithful representation of the scene that we see—but every image is more or less blurry. Thus, image deblurring is fundamental in making pictures sharp and useful. A digital image is composed of picture elements called pixels. Each pixel is assigned an intensity, meant to characterize the color of a small rectangular segment of the scene....

Cameras often record blurred images of the original object. Restoration of the image is not a trivial procedure, and it can be very expensive for large images. In this project we are trying to efficiently compute confidence intervals for the digital values that represent the image and visualize them so that the viewer can distinguish truth from uncertainty.