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This paper proposes a spectral-graph-based algorithm for face image repairing, which can improve the recognition performance on occluded faces. The face completion algorithm proposed in this paper includes three main procedures: 1) sparse representation for partially occluded face classification; 2) image-based data mining; and 3) graph Laplace (GL) for face image completion. The novel part of the proposed framework is GL, as named from graphical models and the Laplace equation, and can achieve a high-quality repairing of damaged or occluded faces. The relationship between the GL and the traditional Poisson equation is proven. We apply our face repairing algorithm to produce completed faces, and use face recognition to evaluate the performance of the algorithm. Experimental results verify the effectiveness of the GL method for occluded face completion. [ABSTRACT FROM PUBLISHER]

Liquid crystal display (LCD) devices are well known for their slow responses due to the physical limitations of liquid crystals. Therefore, fast moving objects in a scene are often perceived as blurred. This effect is known as the LCD motion blur. In order to reduce LCD motion blur, an accurate LCD model and an efficient deblurring algorithm are needed. However, existing LCD motion blur models are insufficient to reflect the limitation of human-eye-tracking system. Also, the spatiotemporal equivalence in LCD motion blur models has not been proven directly in the discrete 2-D spatial domain, although it is widely used. There are three main contributions of this paper: modeling, analysis, and algorithm. First, a comprehensive LCD motion blur model is presented, in which human-eye-tracking limits are taken into consideration. Second, a complete analysis of spatiotemporal equivalence is provided and verified using real video sequences. Third, an LCD motion blur reduction algorithm is proposed. The proposed algorithm solves an l_1-norm regularized least-squares minimization problem using a subgradient projection method. Numerical results show that the proposed algorithm gives higher peak SNR, lower temporal error, and lower spatial error than motion-compensated inverse filtering and Lucy–Richardson deconvolution algorithm, which are two state-of-the-art LCD deblurring algorithms. [ABSTRACT FROM PUBLISHER]

This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources. Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing, but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous state of the art on validation images. [ABSTRACT FROM PUBLISHER]

In this paper, an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with nonuniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colors under the International Commission on Illumination standard illuminants and obtained a good performance. It was also tested for color-based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard color spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant color-based object recognition and skin detection. [ABSTRACT FROM AUTHOR]

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm, where each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements that it provides over existing techniques for model error compensation in SAR. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to the image blind super-resolution (BSR) problem in the case of affine interframe motion. Although the tasks of image registration, blur identification, and high-resolution (HR) image reconstruction are coupled in the imaging process, when dealing with nonisometric interframe motion or without the exact knowledge of the blurring process, classic SR techniques generally have to tackle them (maybe in some combinations) separately. The main difficulty is that state-of-the-art deconvolution methods cannot be straightforwardly generalized to cope with the space-variant motion. We prove that the operators of affine warping and blur commute with some additional transforms and derive an equivalent form of the BSR observation model. Using this equivalent form, we develop an iterative algorithm to jointly estimate the triple-coupled variables, i.e., the motion parameters, blur kernels, and HR image. Experiments on synthetic and real-life images illustrate the performance of the proposed technique in modeling the space-variant degradation process and restoring local textures. [ABSTRACT FROM AUTHOR]

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV minimization problems and binary MRF models has been much explored. This has resulted in some very efficient combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order derivatives have been proposed. The Euler's elastica model is one such higher order model of central importance, which minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization algorithm based upon graph cuts for minimizing the energy in the Euler's elastica model, by simplifying the problem to that of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flow of the energy function, and converges to a minimum point. The numerical experiments show that our new approach is more effective in maintaining smooth visual results while preserving sharp features better than TV models. [ABSTRACT FROM PUBLISHER]

This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample. [ABSTRACT FROM AUTHOR]

Augmented Lagrangian variational formulations and alternating optimization have been adopted to solve distributed parameter estimation problems. The alternating direction method of multipliers (ADMM) is one of such formulations/optimization methods. Very recently, the number of applications of the ADMM, or variants of it, to solve inverse problems in image and signal processing has increased at an exponential rate. The reason for this interest is that ADMM decomposes a difficult optimization problem into a sequence of much simpler problems. In this paper, we use the ADMM to reconstruct piecewise-smooth distributed parameters of elliptical partial differential equations from noisy and linear (blurred) observations of the underlying field. The distributed parameters are estimated by solving an inverse problem with total variation (TV) regularization. The proposed instance of the ADMM solves, in each iteration, an \ell2 and a decoupled \ell2 - \ell1 optimization problems. An operator splitting is used to simplify the treatment of the TV regularizer, avoiding its smooth approximation and yielding a simple yet effective ADMM reconstruction method compared with previously proposed approaches. The competitiveness of the proposed method, with respect to the state-of-the-art, is illustrated in simulated 1-D and 2-D elliptical equation problems, which are representative of many real applications. [ABSTRACT FROM AUTHOR]

We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and n-way furcations, i.e., structures that abound in an arterial network, e.g., a cerebrovascular circulation. Our method is capable of extracting the entire vascular tree from an angiogram in a single execution with a proper initialization. A succinct initial model from the user with arterial network inlets, outlets, and branching points is sufficient for complex vasculature. The novel method is based upon the theory of principal curves. In this paper, theoretical extension to grayscale angiography is discussed, and an algorithm to find an arterial network as principal curves is also described. Quantitative validation on a number of simulated data sets, synthetic volumes of 19 BrainWeb vascular models, and 32 Rotterdam Coronary Artery volumes was conducted. We compared the algorithm to a state-of-the-art method and further tested it on two clinical data sets. Our algorithmic outputs—lumen centers and flow channel widths—are important to various medical and clinical applications, e.g., vasculature segmentation, registration and visualization, virtual angioscopy, and vascular atlas formation and population study. [ABSTRACT FROM AUTHOR]

A family of the block matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patchwise image modeling refid="ref1"/, refid="ref2"/. In this paper, we construct analysis and synthesis frames, formalizing BM3D image modeling, and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem, i.e., one given by the minimization of the single-objective function and another based on the generalized Nash equilibrium (GNE) balance of two objective functions. The latter results in the algorithm where deblurring and denoising operations are decoupled. The convergence of the developed algorithms is proved. Simulation experiments show that the decoupled algorithm derived from the GNE formulation demonstrates the best numerical and visual results and shows superiority with respect to the state of the art in the field, confirming a valuable potential of BM3D-frames as an advanced image modeling tool. [ABSTRACT FROM AUTHOR]

This paper introduces a class of adaptive Perona–Malik (PM) diffusion, which combines the PM equation with the heat equation. The PM equation provides a potential algorithm for image segmentation, noise removal, edge detection, and image enhancement. However, the defect of traditional PM model is tending to cause the staircase effect and create new features in the processed image. Utilizing the edge indicator as a variable exponent, we can adaptively control the diffusion mode, which alternates between PM diffusion and Gaussian smoothing in accordance with the image feature. Computer experiments indicate that the present algorithm is very efficient for edge detection and noise removal. [ABSTRACT FROM AUTHOR]

The logarithmic image processing (LIP) model is a mathematical theory providing generalized linear operations for image processing. The gigavision sensor (GVS) is a new imaging device that can be described by a statistical model. In this paper, by studying these two seemingly unrelated models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS. A new parametric LIP model is also developed. To illustrate the application of the new scalar multiplication operation, we propose an energy-preserving algorithm for tone mapping, which is a necessary step in image dehazing. By comparing with results using two state-of-the-art algorithms, we show that the new scalar multiplication operation is an effective tool for tone mapping. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new demosaicing algorithm that can be used for various sensor images captured by digital cameras equipped with various red–green–blue color filter arrays. Our algorithm enhances the universal demosaicing algorithm of Lukac et al by defining a new spectral interpolation model that exploits not only the information on the color of pixels but also the relative distance between neighboring pixels within an image. Moreover, we include an edge-detection model that makes our algorithm adaptive and reduces the presence of color shifts and artifacts. A series of tests has been made on images of the Kodak database, and our algorithm performs better than the universal demosaicing algorithm with regard to both subjective and objective evaluation. The versatility of our demosaicing algorithm is also highlighted through an application to the issue of color image resampling, and we obtain conclusive experimental results. [ABSTRACT FROM PUBLISHER]

This paper studies a problem of image restoration that observed images are contaminated by Gaussian and impulse noise. Existing methods for this problem in the literature are based on minimizing an objective functional having the \ell^1 fidelity term and the Mumford–Shah regularizer. We present an algorithm on this problem by minimizing a new objective functional. The proposed functional has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the \ell^1 and \ell^2 norms. The regularizer in the functional is formed by the \ell^1 norm of tight framelet coefficients of the underlying image. The selected tight framelet filters are able to extract geometric features of images. We then propose an iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional. Parameters in IFASDA are adaptively varying at each iteration and are determined automatically. In this sense, IFASDA is a parameter-free algorithm. This advantage makes the algorithm more attractive and practical. The effectiveness of IFASDA is experimentally illustrated on problems of image deblurring with Gaussian and impulse noise. Improvements in both PSNR and visual quality of IFASDA over a typical existing method are demonstrated. In addition, Fast_IFASDA, an accelerated algorithm of IFASDA, is also developed. [ABSTRACT FROM AUTHOR]

This paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fidelity term that measures the conformity of spatial and frequency data within each region to (generalized) Gaussian densities whose parameters are determined jointly with the segmentation process. Compared with the classical region competition model, our approach gives soft segmentation results via the fuzzy membership functions, and moreover, the use of frequency data provides additional region information that can improve the overall segmentation result. To efficiently solve the minimization of the energy functional, we adopt an alternate minimization procedure and make use of Chambolle's fast duality projection algorithm. We apply the proposed method to synthetic and natural textures as well as real-world natural images. Experimental results show that our proposed method has very promising segmentation performance compared with the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

When dealing with motion blur, there is an inevitable tradeoff between the amount of blur and the amount of noise in the acquired images. The effectiveness of any restoration algorithm typically depends on these amounts, and it is difficult to find their best balance in order to ease the restoration task. To face this problem, we provide a methodology for deriving a statistical model of the restoration performance of a given deblurring algorithm in case of arbitrary motion. Each restoration-error model allows us to investigate how the restoration performance of the corresponding algorithm varies as the blur due to motion develops. Our modeling treats the point-spread-function trajectories as random processes and, following a Monte Carlo approach, expresses the restoration performance as the expectation of the restoration error conditioned on some motion-randomness descriptors and on the exposure time. This allows us to coherently encompass various imaging scenarios, including camera shake and uniform (rectilinear) motion, and, for each of these, identify the specific exposure time that maximizes the image quality after deblurring. [ABSTRACT FROM AUTHOR]

In this correspondence, our goal is to develop a visual tracking algorithm that is able to track moving objects in the presence of illumination variations in the scene and that is robust to occlusions. We treat the illumination and motion (x\ -y translation and scale) parameters as the unknown “state” sequence. The observation is the entire image, and the observation model allows for occasional occlusions (modeled as outliers). The nonlinearity and multimodality of the observation model necessitate the use of a particle filter (PF). Due to the inclusion of illumination parameters, the state dimension increases, thus making regular PFs impractically expensive. We show that the recently proposed approach using a PF with a mode tracker can be used here since, even in most occlusion cases, the posterior of illumination conditioned on motion and the previous state is unimodal and quite narrow. The key idea is to importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination. Experiments demonstrate the advantage of the proposed algorithm over existing PF-based approaches for various face and vehicle tracking. We are also able to detect illumination model changes, e.g., those due to transition from shadow to sunlight or vice versa by using the generalized expected log-likelihood statistics and successfully compensate for it without ever loosing track. [ABSTRACT FROM AUTHOR]