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301
Deterministic edgepreserving regularization in computed imaging
 IEEE Trans. Image Processing
, 1997
"... Abstract—Many image processing problems are ill posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. In this paper, we first give conditions for the design of such ..."
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Cited by 311 (27 self)
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Abstract—Many image processing problems are ill posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. In this paper, we first give conditions for the design of such an edgepreserving regularization. Under these conditions, we show that it is possible to introduce an auxiliary variable whose role is twofold. First, it marks the discontinuities and ensures their preservation from smoothing. Second, it makes the criterion halfquadratic. The optimization is then easier. We propose a deterministic strategy, based on alternate minimizations on the image and the auxiliary variable. This leads to the definition of an original reconstruction algorithm, called ARTUR. Some theoretical properties of ARTUR are discussed. Experimental results illustrate the behavior of the algorithm. These results are shown in the field of tomography, but this method can be applied in a large number of applications in image processing. I.
Proximal Splitting Methods in Signal Processing
"... The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of inverse problems ..."
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Cited by 266 (31 self)
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The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of inverse problems and, especially, in signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. These proximal splitting methods are shown to capture and extend several wellknown algorithms in a unifying framework. Applications of proximal methods in signal recovery and synthesis are discussed.
Joint MAP registration and highresolution image estimation using a sequence of undersampled images
 IEEE Transactions on Image Processing
, 1997
"... Abstract — In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for esti ..."
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Cited by 219 (2 self)
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Abstract — In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for estimating a highresolution image, with reduced aliasing, from a sequence of undersampled frames. Several approaches to this problem have been investigated previously. However, in this paper a maximum a posteriori (MAP) framework for jointly estimating image registration parameters and the highresolution image is presented. Several previous approaches have relied on knowing the registration parameters a priori or have utilized registration techniques not specifically designed to treat severely aliased images. In the proposed method, the registration parameters are iteratively updated along with the highresolution image in a cyclic coordinatedescent optimization procedure. Experimental results are provided to illustrate the performance of the proposed MAP algorithm using both visible and infrared images. Quantitative error analysis is provided and several images are shown for subjective evaluation. Index Terms—Aliased, high resolution, image registration, image sequence, MAP estimation. I.
Multiresolution markov models for signal and image processing
 Proceedings of the IEEE
, 2002
"... This paper reviews a significant component of the rich field of statistical multiresolution (MR) modeling and processing. These MR methods have found application and permeated the literature of a widely scattered set of disciplines, and one of our principal objectives is to present a single, coheren ..."
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Cited by 153 (17 self)
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This paper reviews a significant component of the rich field of statistical multiresolution (MR) modeling and processing. These MR methods have found application and permeated the literature of a widely scattered set of disciplines, and one of our principal objectives is to present a single, coherent picture of this framework. A second goal is to describe how this topic fits into the even larger field of MR methods and concepts–in particular making ties to topics such as wavelets and multigrid methods. A third is to provide several alternate viewpoints for this body of work, as the methods and concepts we describe intersect with a number of other fields. The principle focus of our presentation is the class of MR Markov processes defined on pyramidally organized trees. The attractiveness of these models stems from both the very efficient algorithms they admit and their expressive power and broad applicability. We show how a variety of methods and models relate to this framework including models for selfsimilar and 1/f processes. We also illustrate how these methods have been used in practice. We discuss the construction of MR models on trees and show how questions that arise in this context make contact with wavelets, state space modeling of time series, system and parameter identification, and hidden
Spatial resolution properties of penalizedlikelihood image reconstruction methods:
 Spaceinvariant tomographs,” IEEE Trans. Image Processing,
, 1996
"... ABSTRACT This paper examines the spatial resolution properties of penalizedlikelihood image reconstruction methods by analyzing the local impulse response. The analysis shows that standard regularization penalties induce spacevariant local impulse response functions, even for spaceinvariant tomo ..."
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Cited by 148 (71 self)
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ABSTRACT This paper examines the spatial resolution properties of penalizedlikelihood image reconstruction methods by analyzing the local impulse response. The analysis shows that standard regularization penalties induce spacevariant local impulse response functions, even for spaceinvariant tomographic systems. Paradoxically, for emission image reconstruction the local resolution is generally poorest in highcount regions. We show that the linearized local impulse response induced by quadratic roughness penalties depends on the object only through its projections. This analysis leads naturally to a modified regularization penalty that yields reconstructed images with nearly uniform resolution. The modified penalty also provides a very practical method for choosing the regularization parameter to obtain a specified resolution in images reconstructed by penalizedlikelihood methods.
Analysis versus synthesis in signal priors
, 2005
"... The concept of prior probability for signals plays a key role in the successful solution of many inverse problems. Much of the literature on this topic can be divided between analysisbased and synthesisbased priors. Analysisbased priors assign probability to a signal through various forward measu ..."
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Cited by 147 (16 self)
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The concept of prior probability for signals plays a key role in the successful solution of many inverse problems. Much of the literature on this topic can be divided between analysisbased and synthesisbased priors. Analysisbased priors assign probability to a signal through various forward measurements of it, while synthesisbased priors seek a reconstruction of the signal as a combination of atom signals. In this paper we describe these two prior classes, focusing on the distinction between them. We show that although when reducing to the complete and undercomplete formulations the two become equivalent, in their more interesting overcomplete formulation the two types depart. Focusing on the ℓ1 denoising case, we present several ways of comparing the two types of priors, establishing the existence of an unbridgeable gap between them. 1.
A unified approach to statistical tomography using coordinate descent optimization
 IEEE Trans. on Image Processing
, 1996
"... Abstract 1 Over the past ten years there has been considerable interest in statistically optimal reconstruction of image crosssections from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. ..."
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Cited by 140 (27 self)
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Abstract 1 Over the past ten years there has been considerable interest in statistically optimal reconstruction of image crosssections from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem, most existing reconstruction algorithms take an indirect approach based on the expectation maximization (EM) algorithm. In this paper we propose a new approach to statistically optimal image reconstruction based on direct optimization of the MAP criterion. The key to this direct optimization approach is greedy pixelwise computations known as iterative coordinate decent (ICD). We show that the ICD iterations require approximately the same amount of computation per iteration as EM based approaches, but the new method converges much more rapidly (in our experiments typically 5 iterations). Other advantages of the ICD method are that it is easily applied to MAP estimation of transmission tomograms, and typical convex constraints, such as positivity, are simply incorporated.
MINIMIZERS OF COSTFUNCTIONS INVOLVING NONSMOOTH DATAFIDELITY TERMS. APPLICATION TO THE PROCESSING OF OUTLIERS
, 2002
"... We present a theoretical study of the recovery of an unknown vector x ∈ Rp (such as a signal or an image) from noisy data y ∈ Rq by minimizing with respect to x a regularized costfunction F(x, y) = Ψ(x, y) + αΦ(x), where Ψ is a datafidelity term, Φ is a smooth regularization term, and α> 0 i ..."
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Cited by 105 (19 self)
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We present a theoretical study of the recovery of an unknown vector x ∈ Rp (such as a signal or an image) from noisy data y ∈ Rq by minimizing with respect to x a regularized costfunction F(x, y) = Ψ(x, y) + αΦ(x), where Ψ is a datafidelity term, Φ is a smooth regularization term, and α> 0 is a parameter. Typically, Ψ(x, y) = ‖Ax − y‖2, where A is a linear operator. The datafidelity terms Ψ involved in regularized costfunctions are generally smooth functions; only a few papers make an exception to this and they consider restricted situations. Nonsmooth datafidelity terms are avoided in image processing. In spite of this, we consider both smooth and nonsmooth datafidelity terms. Our goal is to capture essential features exhibited by the local minimizers of regularized costfunctions in relation to the smoothness of the datafidelity term. In order to fix the context of our study, we consider Ψ(x, y) = i ψ(aTi x − yi), where aTi are the rows of A and ψ is Cm on R \ {0}. We show that if ψ′(0−) < ψ′(0+), then typical data y give rise to local minimizers x ̂ of F(., y) which fit exactly a certain number of the data entries: there is a possibly large set h ̂ of indexes such that aTi x ̂ = yi for every i ∈ ĥ. In contrast, if ψ is
InformationTheoretic Analysis of Interscale and Intrascale Dependencies Between Image Wavelet Coefficients
 IEEE Transactions on Image Processing
, 2001
"... This paper presents an informationtheoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance. ..."
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Cited by 99 (1 self)
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This paper presents an informationtheoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance.
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
, 2006
"... This paper deals with the total variation minimization problem in image restoration for convex data fidelity functionals. We propose a new and fast algorithm which computes an exact solution in the discrete framework. Our method relies on the decomposition of an image into its level sets. It maps ..."
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Cited by 94 (9 self)
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This paper deals with the total variation minimization problem in image restoration for convex data fidelity functionals. We propose a new and fast algorithm which computes an exact solution in the discrete framework. Our method relies on the decomposition of an image into its level sets. It maps the original problems into independent binary Markov Random Field optimization problems at each level. Exact solutions of these binary problems are found thanks to minimum cost cut techniques in graphs. These binary solutions are proved to be monotone increasing with levels and yield thus an exact solution of the discrete original problem. Furthermore we show that minimization of total variation under L1 data fidelity term yields a selfdual contrast invariant filter. Finally we present some results.