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.
Plugandplay priors for model based reconstruction
 in: Proceedings of Int. Conf. Global Signal and Information Processing
"... AbstractModelbased 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 prog ..."
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AbstractModelbased 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.
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.
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.
Wavelet Bayesian Network Image Denoising
"... Abstract — From the perspective of the Bayesian approach, the denoising problem is essentially a prior probability modeling and estimation task. In this paper, we propose an approach that exploits a hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability of the ..."
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Abstract — From the perspective of the Bayesian approach, the denoising problem is essentially a prior probability modeling and estimation task. In this paper, we propose an approach that exploits a hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability of the original image. Then, we use the belief propagation (BP) algorithm, which estimates a coefficient based on all the coefficients of an image, as the maximumaposterior (MAP) estimator to derive the denoised wavelet coefficients. We show that if the network is a spanning tree, the standard BP algorithm can perform MAP estimation efficiently. Our experiment results demonstrate that, in terms of the peaksignaltonoiseratio and perceptual quality, the proposed approach outperforms stateoftheart algorithms on several images, particularly in the textured regions, with various amounts of white Gaussian noise. Index Terms — Bayesian network, image denoising, wavelet transform.
APPROXIMATE BAYESIAN COMPUTATION, STOCHASTIC ALGORITHMS AND NONLOCAL MEANS FOR COMPLEX NOISE MODELS
, 2015
"... algorithms and nonlocal means for complex noise models ..."
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Adaptive Nonlocal Signal Restoration and Enhancement Techniques for HighDimensional Data
"... The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition pro ..."
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The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition process based on the measurement of a physical quantity of interest incident upon an imaging sensor over a specified period of time. The quantity of interest depends on the targeted imaging application. Common imaging sensors measure the number of photons impinging over a dense grid of photodetectors in order to produce an image similar to what is perceived by the human visual system. Di↵erent applications focus on the part of the electromagnetic spectrum not visible by the human visual system, and thus require di↵erent sensing technologies to form the image. In all cases, even with the advance of technology, raw data is invariably a↵ected