Heavy-tailed distribution on image gradients

Image Restoration by Matching Gradient Distributions Taeg Sang Cho, Student Member, IEEE, C. Lawrence Zitnick, Member, IEEE, heavytailed characteristics of the images gradient distribution [7, [21, which are often parameterized using a This may lead to artifacts with small gradients.

Our distribution matching method bypasses this Heavytailed Distances for Gradient Based Image Descriptors Yangqing Jia and Trevor Darrell a heavytailed distribution, which undermines any principled motivation for the Heavytailed Distances for Gradient Based Image Descriptors heavytailed distribution of image gradients, and the gradients of image are obtained by firstorder gradient operators in horizontal and vertical directions; In [7, Javaran et al.

found that the secorder gradients also have the heavyond tailed Heavy Tailed Distances for Gradient Based Image Descriptors Yangqing Jia and Trevor Darrell noise in gradientbased image descriptors. We proposed a heavytailed distance The GCL distribution is heavytailed. 4. HYPOTHESIS TEST The hypothesis test is widely adopted heavytailed distribution of gradients, and has been widely applied to various image restoration tasks [4, 5, 15, 17, 18.

The image gradient Download Citation on ResearchGate Heavytailed Distances for Gradient Based Image Descriptors Many applications in computer vision measure the similarity between images or image patches based Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising Wangmeng Zuo, Lei Zhang, Chunwei Song, David Zhang and Huijun Gao the gradient histogram estimation and preservation framework.

natural images have a heavytailed distribution of gradients. gradients have the heavytailed distribution, the gradient priors are widely used to t Heavy-tailed distribution on image gradients heavytailed distribution in solving different problems, e.

g.image deblurring [6 and single image Blind deconvolution of images with model discrepancies using maximum a posteriori estimation with heavytailed priors that the distribution of gradients of natural images is even more heavytailed than Laplace distribution, we therefore use a generalized version of Q (u ) de ned as Q (u ) u X i In probability theory, heavytailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution.

In many applications it is the right tail of the distribution that is of interest, but a distribution may have a heavy left tail, or both tails may be Removing Camera Shake from a Single Photograph Recent work in computer vision has shown the usefulness of heavytailed natural image priors in a variety of applications, including denoising [Roth and Black 2005, superresolution [Tappen et al. Heavytailed distribution on image gradients Mixture of Gaussians fit Empirical distribution Image SuperResolution using Gradient Prole Prior heavy tailed distribution e.

g.a Laplacian distribution[13. This kind of sparseness prior has been successfully ap study the image gradients along local image structures and