The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent works focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structures on estimating blur kernels. What is the useful image structure and how can one select good regions...

We propose a very fast and effective one-step restoring method for blurry face images. In the last decades, many blind deblurring algorithms have been proposed to restore latent sharp images. However, these algorithms run slowly because of involving two steps: kernel estimation and following non-blind deconvolution or latent image estimation. Also they cannot handle face images in small size. O...

Image blur kernel classification and parameter estimation are critical for blind image deblurring. Current dominant approaches use handcrafted blur features [5, 6] that are optimized for a certain type of blur, which is not applicable in real blind deconvolution where the Point Spread Function (PSF) of the blur is unknown. Inspired by the successful applications of deep learning techniques to o...

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...

A deformable mirror is used to scan the focal plane during the camera exposure, obtaining extended depth-of-field. A deconvolution kernel is approximated for a given scan depth and used to de-blur the image. OCIS codes: (180.2520) Fluorescence microscopy; (110.1080) Active or adaptive optics; (100.1830) Deconvolution

Recovering an un-blurred image from a single motion-blurred picture has long been a fundamental research problem. If one assumes that the blur kernel – or point spread function (PSF) – is shift invariant, the problem reduces to that of image deconvolution. Image deconvolution can be further categorized as non-blind and blind. In non-blind deconvolution, the motion blur kernel is assumed to be k...

Sparse blind deconvolution is the problem of estimating the blur kernel and sparse excitation, both of which are unknown. Considering a linear convolution model, as opposed to the standard circular convolution model, we derive a sufficient condition for stable deconvolution. The columns of the linear convolution matrix form a Riesz basis with the tightness of the Riesz bounds determined by the ...

Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the sharp image and blur kernel are required to regularize the solution space. While this naturally leads to a standard MAP estimation framework, performance is compromised by unknown trade-off parameter settings, optimiza...

This thesis investigates the removal of spatially-variant blur from photographs degraded by camera shake, and the removal of large occluding objects from photographs of popular places. We examine these problems in the case where the photographs are taken with standard consumer cameras, and we have no particular information about the scene being photographed. Most existing deblurring methods mod...