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Fast O(1) Bilateral Filtering Using Trigonometric Range Kernels

Related publications (36)

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Optimally Localized Wavelets and Smoothing Kernels

Kunal Narayan Chaudhury

It is well-known that the Gaussian functions and, more generally, their modulations-translations (the Gabor functions) have the unique property of being optimally localized in space and frequency in the sense of Heisenberg's uncertainty principle. In this ...

EPFL2011

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

A Wideband Doubly-Sparse Approach For MITO Sparse Filter Estimation

Rémi Gribonval

We propose an approach for the estimation of sparse filters from a convolutive mixture of sources, exploiting the time-domain sparsity of the mixing filters and the sparsity of the sources in the time-frequency (TF) domain. The proposed approach is based o ...

IEEE2011

Filter Learning for Linear Structure Segmentation

Pascal Fua, Engin Türetken, Vincent Lepetit, Germán González Serrano, Roberto Rigamonti

We introduce an approach to learning convolution filters whose joint output can be fed to a classifier that labels them as belonging to linear structures or not. The filters are learned using sparse synthesis techniques but we show that enforcing constrain ...

2011

Methods and apparatus for estimating a sparse channel

Martin Vetterli, Ali Hormati, Yann Barbotin

Embodiments include a method for sending a selected number of pilots (20) to a sparse channel having a channel impulse response limited in time comprising sending the selected number of the pilots (20). The pilots (20) are equally spaced in the frequency d ...

USPTO2011

Fast Space-Variant Elliptical Filtering Using Box Splines

Michaël Unser, Kunal Narayan Chaudhury

The efficient realization of linear space-variant (non-convolution) filters is a challenging computational problem in image processing. In this paper, we demonstrate that it is possible to filter an image with a Gaussian-like elliptic window of varying siz ...

IEEE2010

Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation

Martin Vetterli, Yue Lu, Mina Karzand

Color image demosaicking is a key process in the digital imaging pipeline. In this paper, we study a well-known and influential demosaicking algorithm based on alternating projections (AP), proposed by Gunturk, Altunbasak and Mersereau in 2002. Since its p ...

Institute of Electrical and Electronics Engineers2010

Double Sparsity: Towards Blind Estimation of Multiple Channels

Rémi Gribonval

We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the time-domain sparsity of the mixing filters and the disjointness of the sources in the time-frequency domain. The proposed framework includes two steps: (a ...

Springer-Verlag's2010

A union of incoherent spaces model for classification

Pierre Vandergheynst, Karin Schnass

We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low miscla ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

The Subconvexity Problem For Gl(2)

Philippe Michel

Generalizing and unifying prior results, we solve the subconvexity problem for the L-functions of GL(1) and GL(2) automorphic representations over a fixed number field, uniformly in all aspects. A novel feature of the present method is the softness of our ...

Springer-Verlag2010