We analyze a variational method for reconstructing a bar code signal from a blurry and noisy measurement. The bar code is modeled as a binary function with a finite number of transitions and a parameter controlling minimal feature size. The measured signal is the convolution of this binary function with a Gaussian kernel. In this work, we assume that the blur kernel is known and establish condi...

This article presents Local Fourier Burst Accumulation, a recent motion deblurring method. This method processes image bursts and can be naturally extended to video deblurring. The algorithm first registers the frame to its neighboring frames by consistency-controlled optical flow and then fuses the frames temporally by a weighted average in the Fourier domain. As the method does not require th...

Nonparametric kernel estimators are widely used in many research areas of statistics. An important nonparametric kernel estimator of a regression function is the Nadaraya-Watson kernel regression estimator which is often obtained by using a fixed bandwidth. However, the adaptive kernel estimators with varying bandwidths are specially used to estimate density of the long-tailed and multi-mod dis...

In most typical digital cameras, even high-end digital single lens reflex ones (dslr), the acquired images are sampled at rates below the Nyquist critical rate, causing aliasing effects. In this work we describe a new algorithm for the estimation of the point spread function (psf) of a digital camera from aliased photographs, that achieves subpixel accuracy. The procedure is based on taking two...

Image deconvolution is a challenging ill-posed problem when only partial information of the blur kernel is available. Certain regularization on sharp images has to be imposed to constrain the estimation of true images during the blind deconvolution process. Based on the observation that an image of sharp edges tends to minimize the ratio between the `1 norm and the `2 norm of its wavelet frame ...

We consider the problem of depth estimation from multiple images based on the defocus cue. For a Gaussian defocus blur, the observations can be shown to be the solution of a deterministic but inhomogeneous diffusion process. However, the diffusion process does not sufficiently address the case in which the Gaussian kernel is deformed. This deformation happens due to several factors like self-oc...

This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions.

Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant to blur which is often prevalent in real image data. From previous work, we know that most state-of-the-art texture feature extraction methods are unable to ...

Contrary to common assumptions in the literature, the blur kernel corresponding to lens-effect blur has been demonstrated to be spatially-varying across the image plane. Existing models for the corresponding point spread function (PSF) are either parameterized and spatially-invariant, or spatially-varying but ad-hoc and discretelydefined. In this paper, we develop and present a novel, spatially...