Results 1  10
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27
Global image denoising
 IEEE Trans. on Image Proc
, 2014
"... Abstract — Most existing stateoftheart image denoising algorithms are based on exploiting similarity between a relatively modest number of patches. These patchbased methods are strictly dependent on patch matching, and their performance is hamstrung by the ability to reliably find sufficiently ..."
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Abstract — Most existing stateoftheart image denoising algorithms are based on exploiting similarity between a relatively modest number of patches. These patchbased methods are strictly dependent on patch matching, and their performance is hamstrung by the ability to reliably find sufficiently similar patches. As the number of patches grows, a point of diminishing returns is reached where the performance improvement due to more patches is offset by the lower likelihood of finding sufficiently close matches. The net effect is that while patchbased methods, such as BM3D, are excellent overall, they are ultimately limited in how well they can do on (larger) images with increasing complexity. In this paper, we address these shortcomings by developing a paradigm for truly global filtering where each pixel is estimated from all pixels in the image. Our objectives in this paper are twofold. First, we give a statistical analysis of our proposed global filter, based on a spectral decomposition of its corresponding operator, and we study the effect of truncation of this spectral decomposition. Second, we derive an approximation to the spectral (principal) components using the Nyström extension. Using these, we demonstrate that this global filter can be implemented efficiently by sampling a fairly small percentage of the pixels in the image. Experiments illustrate that our strategy can effectively globalize any existing denoising filters to estimate each pixel using all pixels in the image, hence improving upon the best patchbased methods. Index Terms — Image denoising, nonlocal filters, Nyström extension, spatial domain filter, risk estimator.
BILATERAL FILTER: GRAPH SPECTRAL INTERPRETATION AND EXTENSIONS
"... In this paper we study the bilateral filter proposed by Tomasi and Manduchi and show that it can be viewed as a spectral domain transform defined on a weighted graph. The nodes of this graph represent the pixels in the image and a graph signal defined on the nodes represents the intensity values. ..."
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Cited by 4 (1 self)
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In this paper we study the bilateral filter proposed by Tomasi and Manduchi and show that it can be viewed as a spectral domain transform defined on a weighted graph. The nodes of this graph represent the pixels in the image and a graph signal defined on the nodes represents the intensity values. Edge weights in the graph correspond to the bilateral filter coefficients and hence are data adaptive. The graph spectrum is defined in terms of the eigenvalues and eigenvectors of the graph Laplacian matrix. We use this spectral interpretation to generalize the bilateral filter and propose new spectral designs of “bilaterallike ” filters. We show that these spectral filters can be implemented with kiterative bilateral filtering operations and do not require expensive diagonalization of the Laplacian matrix. Index Terms — Bilateral filter, graph based signal processing, polynomial approximation 1.
REGRESSION FRAMEWORK FOR BACKGROUND ESTIMATION IN REMOTE SENSING IMAGERY
"... A key component in any target or anomaly detection algorithm is the characterization of the background. We investigate several approaches for estimating the background level at a given pixel, based on both the local neighborhood around that pixel and on the global context of the full image. By frami ..."
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A key component in any target or anomaly detection algorithm is the characterization of the background. We investigate several approaches for estimating the background level at a given pixel, based on both the local neighborhood around that pixel and on the global context of the full image. By framing this as a regression problem, we can compare a variety of background estimation schemes, from standard signal processing approaches long used in the hyperspectral image analysis community to more sophisticated nonlinear approaches that have recently been developed in the image processing community. These comparisons are performed on a range of images including single band, standard redgreenblue, eightband WorldView2, and 126band hyperspectral HyMap imagery.
Crossscale cost aggregation for stereo matching
 In CVPR
, 2014
"... Human beings process stereoscopic correspondence across multiple scales. However, this bioinspiration is ignored by stateoftheart cost aggregation methods for dense stereo correspondence. In this paper, a generic crossscale cost aggregation framework is proposed to allow multiscale interactio ..."
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Human beings process stereoscopic correspondence across multiple scales. However, this bioinspiration is ignored by stateoftheart cost aggregation methods for dense stereo correspondence. In this paper, a generic crossscale cost aggregation framework is proposed to allow multiscale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an interscale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the crossscale framework is important as it effectively and efficiently expands stateoftheart cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets. 1.
PlugandPlay Priors for Model Based Reconstruction
"... Abstract—Modelbased reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progr ..."
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Abstract—Modelbased reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving modelbased inversion when the forward model corresponds to complex physical measurements in applications such as Xray CT, electronmicroscopy, MRI, and ultrasound, to name just a few. However, combining stateoftheart denoising algorithms (i.e., prior models) with stateoftheart inversion methods (i.e., forward models) has been a challenge for many reasons. In this paper, we propose a flexible framework that allows stateoftheart forward models of imaging systems to be matched with stateoftheart priors or denoising models. This framework, which we term as PlugandPlay priors, has the advantage that it dramatically simplifies software integration, and moreover, it allows stateoftheart denoising methods that have no known formulation as an optimization problem to be used. We demonstrate with some simple examples how PlugandPlay priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions. I.
Nonparametric noise estimation method for raw images
, 2013
"... Optimal denoising works at best on raw images (the image formed at the output of the focal plane, at the CCD or CMOS detector), which display a white signaldependent noise. The noise model of the raw image is characterized by a function that given the intensity of a pixel in the noisy image returns ..."
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Optimal denoising works at best on raw images (the image formed at the output of the focal plane, at the CCD or CMOS detector), which display a white signaldependent noise. The noise model of the raw image is characterized by a function that given the intensity of a pixel in the noisy image returns the corresponding standard deviation; the plot of this function is the noise curve. This paper develops a nonparametric approach estimating the noise curve directly from a single raw image. An extensive crossvalidation procedure is described to compare this new method with stateoftheart parametric methods and with laboratory calibration methods giving a reliable ground truth, even for nonlinear detectors. © 2014 Optical Society of America OCIS codes: (040.1520) CCD, chargecoupled device; (100.2960) Image analysis; (110.4280) Noise in imaging systems; (040.0040) Detectors; (040.3780) Low light level; (100.2980) Image enhancement.
A regularization approach to blind deblurring and denoising of qr barcodes
, 2014
"... Abstract — QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularizationbased methods for blind deblurring of QR bar c ..."
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Abstract — QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularizationbased methods for blind deblurring of QR bar codes in the presence of noise. Index Terms — QR bar code, blind deblurring, finder pattern, TV regularization, TV flow. I.
Reconstruction
, 2013
"... Modelbased reconstruction is a powerful framework for solving a variety of inverse problems in imaging. The method works by combining a forward model of the imaging system with a prior model of the image itself, and the reconstruction is then computed by minimizing a functional consisting of the su ..."
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Modelbased reconstruction is a powerful framework for solving a variety of inverse problems in imaging. The method works by combining a forward model of the imaging system with a prior model of the image itself, and the reconstruction is then computed by minimizing a functional consisting of the sum of two terms corresponding to the forward and prior models. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. A wide range of methods including nonlocal means, dictionarybased methods, 3D block matching, TV minimization and kernelbased filtering have proven that it is possible to recover high fidelity images even after a great deal of noise has been added. Similarly, great progress has been made in improving modelbased inversion when the forward model corresponds to complex physical measurements in applications such as Xray CT, electronmicroscopy, MRI, and ultrasound, to name just a few. However, combining stateoftheart denoising algorithms (i.e., prior models) with stateoftheart inversion methods (i.e., forward models) has been a challenge for many reasons.
ACCELERATED GRAPHBASED SPECTRAL POLYNOMIAL FILTERS
, 2015
"... Graphbased spectral denoising is a lowpass filtering using the eigendecomposition of the graph Laplacian matrix of anoisy signal. Polynomial filtering avoids costly computation of the eigendecomposition by projections onto suitable Krylov subspaces. Polynomial filters can be based, e.g.,on the bil ..."
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Graphbased spectral denoising is a lowpass filtering using the eigendecomposition of the graph Laplacian matrix of anoisy signal. Polynomial filtering avoids costly computation of the eigendecomposition by projections onto suitable Krylov subspaces. Polynomial filters can be based, e.g.,on the bilateral and guided filters. We propose constructing accelerated polynomial filters by running flexible Krylov subspace based linear and eigenvalue solvers such as the Block Locally Optimal Preconditioned Conjugate Gradient(LOBPCG) method.