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Image Processing, IEEE Transactions on

Issue 10 • Date Oct. 2007

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Displaying Results 1 - 24 of 24
  • Table of contents

    Page(s): C1 - C4
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  • IEEE Transactions on Image Processing publication information

    Page(s): C2
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  • Image-Dependent Gamut Mapping as Optimization Problem

    Page(s): 2401 - 2410
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1001 KB) |  | HTML iconHTML  

    We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests. View full abstract»

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  • An Expanded Theoretical Treatment of Iteration-Dependent Majorize-Minimize Algorithms

    Page(s): 2411 - 2422
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (478 KB) |  | HTML iconHTML  

    The majorize-minimize (MM) optimization technique has received considerable attention in signal and image processing applications, as well as in statistics literature. At each iteration of an MM algorithm, one constructs a tangent majorant function that majorizes the given cost function and is equal to it at the current iterate. The next iterate is obtained by minimizing this tangent majorant function, resulting in a sequence of iterates that reduces the cost function monotonically. A well-known special case of MM methods are expectation-maximization algorithms. In this paper, we expand on previous analyses of MM, due to Fessler and Hero, that allowed the tangent majorants to be constructed in iteration-dependent ways. Also, this paper overcomes an error in one of those earlier analyses. There are three main aspects in which our analysis builds upon previous work. First, our treatment relaxes many assumptions related to the structure of the cost function, feasible set, and tangent majorants. For example, the cost function can be nonconvex and the feasible set for the problem can be any convex set. Second, we propose convergence conditions, based on upper curvature bounds, that can be easier to verify than more standard continuity conditions. Furthermore, these conditions allow for considerable design freedom in the iteration-dependent behavior of the algorithm. Finally, we give an original characterization of the local region of convergence of MM algorithms based on connected (e.g., convex) tangent majorants. For such algorithms, cost function minimizers will locally attract the iterates over larger neighborhoods than typically is guaranteed with other methods. This expanded treatment widens the scope of the MM algorithm designs that can be considered for signal and image processing applications, allows us to verify the convergent behavior of previously published algorithms, and gives a fuller understanding overall of how these algorithms behave. View full abstract»

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  • A Multiscale Framework for Spatial Gamut Mapping

    Page(s): 2423 - 2435
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1652 KB) |  | HTML iconHTML  

    Image reproduction devices, such as displays or printers, can reproduce only a limited set of colors, denoted the color gamut. The gamut depends on both theoretical and technical limitations. Reproduction device gamuts are significantly different from acquisition device gamuts. These facts raise the problem of reproducing similar color images across different devices. This is well known as the gamut mapping problem. Gamut mapping algorithms have been developed mainly using colorimetric pixel-wise principles, without considering the spatial properties of the image. The recently proposed multilevel gamut mapping approach takes spatial properties into account and has been demonstrated to outperform spatially invariant approaches. However, they have some important drawbacks. To analyze these drawbacks, we build a common framework that encompasses at least two important previous multilevel gamut mapping algorithms. Then, when the causes of the drawbacks are understood, we solve the typical problem of possible hue shifts. Next, we design appropriate operators and functions to strongly reduce both haloing and possible undesired over compression. We use challenging synthetic images, as well as real photographs, to practically show that the improvements give the expected results. View full abstract»

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  • PRISM: A Video Coding Paradigm With Motion Estimation at the Decoder

    Page(s): 2436 - 2448
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (806 KB) |  | HTML iconHTML  

    We describe PRISM, a video coding paradigm based on the principles of lossy distributed compression (also called source coding with side information or Wyner-Ziv coding) from multiuser information theory. PRISM represents a major departure from conventional video coding architectures (e.g., the MPEGx, H.26x families) that are based on motion-compensated predictive coding, with the goal of addressing some of their architectural limitations. PRISM allows for two key architectural enhancements: (1) inbuilt robustness to "drift" between encoder and decoder and (2) the feasibility of a flexible distribution of computational complexity between encoder and decoder. Specifically, PRISM enables transfer of the computationally expensive video encoder motion-search module to the video decoder. Based on this capability, we consider an instance of PRISM corresponding to a near reversal in codec complexities with respect to today's codecs (leading to a novel light encoder and heavy decoder paradigm), in this paper. We present encouraging preliminary results on real-world video sequences, particularly in the realm of transmission losses, where PRISM exhibits the characteristic of rapid recovery, in contrast to contemporary codecs. This renders PRISM as an attractive candidate for wireless video applications. View full abstract»

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  • Artistic Edge and Corner Enhancing Smoothing

    Page(s): 2449 - 2462
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3102 KB) |  | HTML iconHTML  

    Two important visual properties of paintings and painting-like images are the absence of texture details and the increased sharpness of edges as compared to photographic images. Painting-like artistic effects can be achieved from photographic images by filters that smooth out texture details, while preserving or enhancing edges and corners. However, not all edge preserving smoothers are suitable for this purpose. We present a simple nonlinear local operator that generalizes both the well known Kuwahara filter and the more general class of filters known in the literature as ldquocriterion and value filter structure.rdquo This class of operators suffers from intrinsic theoretical limitations which give rise to a dramatic instability in presence of noise, especially on shadowed areas. Such limitations are discussed in the paper and overcome by the proposed operator. A large variety of experimental results shows that the output of the proposed operator is visually similar to a painting. Comparisons with existing techniques on a large set of natural images highlight conditions on which traditional edge preserving smoothers fail, whereas our approach produces good results. In particular, unlike many other well established approaches, the proposed operator is robust to degradations of the input image such as blurring and noise contamination. View full abstract»

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  • Bilateral Filtering of Diffusion Tensor Magnetic Resonance Images

    Page(s): 2463 - 2475
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2680 KB) |  | HTML iconHTML  

    We extend the well-known scalar image bilateral filtering technique to diffusion tensor magnetic resonance images (DTMRI). The scalar version of bilateral image filtering is extended to perform edge-preserving smoothing of DT field data. The bilateral DT filtering is performed in the log-Euclidean framework which guarantees valid output tensors. Smoothing is achieved by weighted averaging of neighboring tensors. Analogous to bilateral filtering of scalar images, the weights are chosen to be inversely proportional to two distance measures: The geometrical Euclidean distance between the spatial locations of tensors and the dissimilarity of tensors. We describe the noniterative DT smoothing equation in closed form and show how interpolation of DT data is treated as a special case of bilateral filtering where only spatial distance is used. We evaluate different DT tensor dissimilarity metrics including the log-Euclidean, the similarity-invariant log-Euclidean, the square root of the J-divergence, and the distance scaled mutual diffusion coefficient. We present qualitative and quantitative smoothing and interpolation results and show their effect on segmentation, for both synthetic DT field data, as well as real cardiac and brain DTMRI data. View full abstract»

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  • An Inpainting- Based Deinterlacing Method

    Page(s): 2476 - 2491
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3694 KB) |  | HTML iconHTML  

    Video is usually acquired in interlaced format, where each image frame is composed of two image fields, each field holding same parity lines. However, many display devices require progressive video as input; also, many video processing tasks perform better on progressive material than on interlaced video. In the literature, there exist a great number of algorithms for interlaced to progressive video conversion, with a great tradeoff between the speed and quality of the results. The best algorithms in terms of image quality require motion compensation; hence, they are computationally very intensive. In this paper, we propose a novel de interlacing algorithm based on ideas from the image in painting arena. We view the lines to interpolate as gaps that we need to in paint. Numerically, this is implemented using a dynamic programming procedure, which ensures a complexity of O(S), where S is the number of pixels in the image. The results obtained with our algorithm compare favorably, in terms of image quality, with state-of-the-art methods, but at a lower computational cost, since we do not need to perform motion field estimation. View full abstract»

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  • Fractional-Order Anisotropic Diffusion for Image Denoising

    Page(s): 2492 - 2502
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4390 KB) |  | HTML iconHTML  

    This paper introduces a new class of fractional-order anisotropic diffusion equations for noise removal. These equations are Euler-Lagrange equations of a cost functional which is an increasing function of the absolute value of the fractional derivative of the image intensity function, so the proposed equations can be seen as generalizations of second-order and fourth-order anisotropic diffusion equations. We use the discrete Fourier transform to implement the numerical algorithm and give an iterative scheme in the frequency domain. It is one important aspect of the algorithm that it considers the input image as a periodic image. To overcome this problem, we use a folded algorithm by extending the image symmetrically about its borders. Finally, we list various numerical results on denoising real images. Experiments show that the proposed fractional-order anisotropic diffusion equations yield good visual effects and better signal-to-noise ratio. View full abstract»

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  • Resolution- Independent Characteristic Scale Dedicated to Satellite Images

    Page(s): 2503 - 2514
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    We study the problem of finding the characteristic scale of a given satellite image. This feature is defined so that it does not depend on the spatial resolution of the image. This is a different problem than achieving scale invariance, as often studied in the literature. Our approach is based on the use of a linear scale space and the total variation (TV). The critical scale is defined as the one at which the normalized TV reaches its maximum. It is shown experimentally, both on synthetic and real data, that the computed characteristic scale is resolution independent. View full abstract»

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  • Adaptive Filtering for Color Filter Array Demosaicking

    Page(s): 2515 - 2525
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1382 KB) |  | HTML iconHTML  

    Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and 2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost. View full abstract»

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  • Efficient Least Squares Multimodal Registration With a Globally Exhaustive Alignment Search

    Page(s): 2526 - 2534
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3444 KB) |  | HTML iconHTML  

    There are many image registration situations in which the initial misalignment of the two images is large. These registration problems, often involving comparison of the two images only within a region of interest (ROI), are difficult to solve. Most intensity-based registration methods perform local optimization of their cost function and often miss the global optimum when the initial misregistration is large. The registration of multimodal images makes the problem even more difficult since it limits the choice of available cost functions. We have developed an efficient method, capable of multimodal rigid-body registration within an ROI, that performs an exhaustive search over all integer translations, and a local search over rotations. The method uses the fast Fourier transform to efficiently compute the sum of squared differences cost function for all possible integer pixel shifts, and for each shift models the relationship between the intensities of the two images using linear regression. Test cases involving medical imaging, remote sensing and forensic science applications show that the method consistently brings the two images into close registration so that a local optimization method should have no trouble fine-tuning the solution. View full abstract»

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  • Segmentation Framework Based on Label Field Fusion

    Page(s): 2535 - 2550
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2419 KB) |  | HTML iconHTML  

    In this paper, we put forward a novel fusion framework that mixes together label fields instead of observation data as is usually the case. Our framework takes as input two label fields: a quickly estimated and to-be-refined segmentation map and a spatial region map that exhibits the shape of the main objects of the scene. These two label fields are fused together with a global energy function that is minimized with a deterministic iterative conditional mode algorithm. As explained in the paper, the energy function may implement a pure fusion strategy or a fusion-reaction function. In the latter case, a data-related term is used to make the optimization problem well posed. We believe that the conceptual simplicity, the small number of parameters, the use of a simple and fast deterministic optimizer that admits a natural implementation on a parallel architecture are among the main advantages of our approach. Our fusion framework is adapted to various computer vision applications among which are motion segmentation, motion estimation and occlusion detection. View full abstract»

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  • Minimum Class Variance Support Vector Machines

    Page(s): 2551 - 2564
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1990 KB) |  | HTML iconHTML  

    In this paper, a modified class of support vector machines (SVMs) inspired from the optimization of Fisher's discriminant ratio is presented, the so-called minimum class variance SVMs (MCVSVMs). The MCVSVMs optimization problem is solved in cases in which the training set contains less samples that the dimensionality of the training vectors using dimensionality reduction through principal component analysis (PCA). Afterward, the MCVSVMs are extended in order to find nonlinear decision surfaces by solving the optimization problem in arbitrary Hilbert spaces defined by Mercer's kernels. In that case, it is shown that, under kernel PCA, the nonlinear optimization problem is transformed into an equivalent linear MCVSVMs problem. The effectiveness of the proposed approach is demonstrated by comparing it with the standard SVMs and other classifiers, like kernel Fisher discriminant analysis in facial image characterization problems like gender determination, eyeglass, and neutral facial expression detection. View full abstract»

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  • A New Fuzzy Color Correlated Impulse Noise Reduction Method

    Page(s): 2565 - 2575
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1356 KB) |  | HTML iconHTML  

    A new impulse noise reduction method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise reduction performance. In this paper, we discuss an alternative technique which gives a good noise reduction performance while much less artefacts are introduced. The main difference between the proposed method and other classical noise reduction methods is that the color information is taken into account to develop (1) a better impulse noise detection method and (2) a noise reduction method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed method provides a significant improvement on other existing filters. View full abstract»

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  • A Stochastic Continuation Approach to Piecewise Constant Reconstruction

    Page(s): 2576 - 2589
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3202 KB) |  | HTML iconHTML  

    We address the problem of reconstructing a piecewise constant 3-D object from a few noisy 2-D line-integral projections. More generally, the theory developed here readily applies to the recovery of an ideal n-D signal (n ges 1) from indirect measurements corrupted by noise. Stabilization of this ill-conditioned inverse problem is achieved with the Potts prior model, which leads to a challenging optimization task. To overcome this difficulty, we introduce a new class of hybrid algorithms that combines simulated annealing with deterministic continuation. We call this class of algorithms stochastic continuation (SC). We first prove that, under mild assumptions, SC inherits the finite-time convergence properties of generalized simulated annealing. Then, we show that SC can be successfully applied to our reconstruction problem. In addition, we look into the concave distortion acceleration method introduced for standard simulated annealing and we derive an explicit formula for choosing the free parameter of the cost function. Numerical experiments using both synthetic data and real radiographic testing data show that SC outperforms standard simulated annealing. View full abstract»

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  • Edge Grouping Combining Boundary and Region Information

    Page(s): 2590 - 2606
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (11365 KB) |  | HTML iconHTML  

    This paper introduces a new edge-grouping method to detect perceptually salient structures in noisy images. Specifically, we define a new grouping cost function in a ratio form, where the numerator measures the boundary proximity of the resulting structure and the denominator measures the area of the resulting structure. This area term introduces a preference towards detecting larger-size structures and, therefore, makes the resulting edge grouping more robust to image noise. To find the optimal edge grouping with the minimum grouping cost, we develop a special graph model with two different kinds of edges and then reduce the grouping problem to finding a special kind of cycle in this graph with a minimum cost in ratio form. This optimal cycle-finding problem can be solved in polynomial time by a previously developed graph algorithm. We implement this edge-grouping method, test it on both synthetic data and real images, and compare its performance against several available edge-grouping and edge-linking methods. Furthermore, we discuss several extensions of the proposed method, including the incorporation of the well-known grouping cues of continuity and intensity homogeneity, introducing a factor to balance the contributions from the boundary and region information, and the prevention of detecting self-intersecting boundaries. View full abstract»

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  • Background Removal of Multiview Images by Learning Shape Priors

    Page(s): 2607 - 2616
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1636 KB) |  | HTML iconHTML  

    Image-based rendering has been successfully used to display 3-D objects for many applications. A well-known example is the object movie, which is an image-based 3-D object composed of a collection of 2-D images taken from many different viewpoints of a 3-D object. In order to integrate image-based 3-D objects into a chosen scene (e.g., a panorama), one has to meet a hard challenge-to efficiently and effectively remove the background from the foreground object. This problem is referred to as multiview images (MVIs) segmentation. Another task requires MVI segmentation is image-based 3-D reconstruction using multiview images. In this paper, we propose a new method for segmenting MVI, which integrates some useful algorithms, including the well-known graph-cut image segmentation and volumetric graph-cut. The main idea is to incorporate the shape prior into the image segmentation process. The shape prior introduced into every image of the MVI is extracted from the 3-D model reconstructed by using the volumetric graph cuts algorithm. Here, the constraint obtained from the discrete medial axis is adopted to improve the reconstruction algorithm. The proposed MVI segmentation process requires only a small amount of user intervention, which is to select a subset of acceptable segmentations of the MVI after the initial segmentation process. According to our experiments, the proposed method can provide not only good MVI segmentation, but also provide acceptable 3-D reconstructed models for certain less-demanding applications. View full abstract»

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  • A Comparative Study of Local Matching Approach for Face Recognition

    Page(s): 2617 - 2628
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1550 KB) |  | HTML iconHTML  

    In contrast to holistic methods, local matching methods extract facial features from different levels of locality and quantify them precisely. To determine how they can be best used for face recognition, we conducted a comprehensive comparative study at each step of the local matching process. The conclusions from our experiments include: (1) additional evidence that Gabor features are effective local feature representations and are robust to illumination changes; (2) discrimination based only on a small portion of the face area is surprisingly good; (3) the configuration of facial components does contain rich discriminating information and comparing corresponding local regions utilizes shape features more effectively than comparing corresponding facial components; (4) spatial multiresolution analysis leads to better classification performance; (5) combining local regions with Borda count classifier combination method alleviates the curse of dimensionality. We implemented a complete face recognition system by integrating the best option of each step. Without training, illumination compensation and without any parameter tuning, it achieves superior performance on every category of the FERET test: near perfect classification accuracy (99.5%) on pictures taken on the same day regardless of indoor illumination variations, and significantly better than any other reported performance on pictures taken several days to more than a year apart. The most significant experiments were repeated on the AR database, with similar results. View full abstract»

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  • IEEE Transactions on Image Processing EDICS

    Page(s): 2629
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    Freely Available from IEEE
  • IEEE Transactions on Image Processing Information for authors

    Page(s): 2630 - 2631
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    Freely Available from IEEE
  • 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'08)

    Page(s): 2632
    Save to Project icon | Request Permissions | PDF file iconPDF (487 KB)  
    Freely Available from IEEE
  • IEEE Signal Processing Society Information

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (31 KB)  
    Freely Available from IEEE

Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003