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Image Processing, IEEE Transactions on

Issue 6 • Date June 2008

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Displaying Results 1 - 25 of 25
  • Table of contents

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

    Page(s): C2
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  • Phase Local Approximation (PhaseLa) Technique for Phase Unwrap From Noisy Data

    Page(s): 833 - 846
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2256 KB) |  | HTML iconHTML  

    The local polynomial approximation (LPA) is a nonparametric regression technique with pointwise estimation in a sliding window. We apply the LPA of the argument of cos and sin in order to estimate the absolute phase from noisy wrapped phase data. Using the intersection of confidence interval (ICI) algorithm, the window size is selected as adaptive pointwise varying. This adaptation gives the phase estimate with the accuracy close to optimal in the mean squared sense. For calculations, we use a Gauss-Newton recursive procedure initiated by the phase estimates obtained for the neighboring points. It enables tracking properties of the algorithm and its ability to go beyond the principal interval (-pi,pi) and to reconstruct the absolute phase from wrapped phase observations even when the magnitude of the phase difference takes quite large values. The algorithm demonstrates a very good accuracy of the phase reconstruction which on many occasion overcomes the accuracy of the state-of-the-art algorithms developed for noisy phase unwrap. The theoretical analysis produced for the accuracy of the pointwise estimates is used for justification of the ICI adaptation algorithm. View full abstract»

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  • Dynamic Denoising of Tracking Sequences

    Page(s): 847 - 856
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2395 KB) |  | HTML iconHTML  

    In this paper, we describe an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part of the algorithm is based on Bayesian wavelet denoising, which has been chosen due to its exceptional ability to incorporate diverse a priori information into the process of image recovery. In particular, we demonstrate that, in dynamic settings, useful statistical priors can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such priors forms the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences. Within the proposed framework, the previous observations of a dynamic scene are employed to enhance its present observation. The mechanism that allows the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information between the images is governed by a Kalman filter that is used for both prediction and estimation of the dynamics of tracked objects. Therefore, in this methodology, the processes of target tracking and image enhancement "collaborate" in an interlacing manner, rather than being applied separately. The dynamic denoising is demonstrated on several examples of SAR imagery. The results demonstrated in this paper indicate a number of advantages of the proposed dynamic denoising over "static" approaches, in which the tracking images are enhanced independently of each other. View full abstract»

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  • Design of Linear Equalizers Optimized for the Structural Similarity Index

    Page(s): 857 - 872
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (18044 KB) |  | HTML iconHTML  

    We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm. View full abstract»

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  • Generalized Face Super-Resolution

    Page(s): 873 - 886
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4124 KB) |  | HTML iconHTML  

    Existing learning-based face super-resolution (hallucination) techniques generate high-resolution images of a single facial modality (i.e., at a fixed expression, pose and illumination) given one or set of low-resolution face images as probe. Here, we present a generalized approach based on a hierarchical tensor (multilinear) space representation for hallucinating high-resolution face images across multiple modalities, achieving generalization to variations in expression and pose. In particular, we formulate a unified tensor which can be reduced to two parts: a global image-based tensor for modeling the mappings among different facial modalities, and a local patch-based multiresolution tensor for incorporating high-resolution image details. For realistic hallucination of unregistered low-resolution faces contained in raw images, we develop an automatic face alignment algorithm capable of pixel-wise alignment by iteratively warping the probing face to its projection in the space of training face images. Our experiments show not only performance superiority over existing benchmark face super-resolution techniques on single modal face hallucination, but also novelty of our approach in coping with multimodal hallucination and its robustness in automatic alignment under practical imaging conditions. View full abstract»

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  • Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

    Page(s): 887 - 896
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8595 KB) |  | HTML iconHTML  

    The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced. View full abstract»

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  • Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation

    Page(s): 897 - 907
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1433 KB) |  | HTML iconHTML  

    In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods. View full abstract»

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  • A Fully Scalable Motion Model for Scalable Video Coding

    Page(s): 908 - 923
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1202 KB) |  | HTML iconHTML  

    Motion information scalability is an important requirement for a fully scalable video codec, especially for decoding scenarios of low bit rate or small image size. So far, several scalable coding techniques on motion information have been proposed, including progressive motion vector precision coding and motion vector field layered coding. However, it is still vague on the required functionalities of motion scalability and how it collaborates flawlessly with other scalabilities, such as spatial, temporal, and quality, in a scalable video codec. In this paper, we first define the functionalities required for motion scalability. Based on these requirements, a fully scalable motion model is proposed along with tailored encoding techniques to minimize the coding overhead of scalability. Moreover, the associated rate distortion optimized motion estimation algorithm will be provided to achieve better efficiency throughout various decoding scenarios. Simulation results will be presented to verify the superiorities of proposed scalable motion model over nonscalable ones. View full abstract»

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  • Prediction by Partial Approximate Matching for Lossless Image Compression

    Page(s): 924 - 935
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (629 KB) |  | HTML iconHTML  

    Context-based modeling is an important step in high-performance lossless data compression. To effectively define and utilize contexts for natural images is, however, a difficult problem. This is primarily due to the huge number of contexts available in natural images, which typically results in higher modeling costs, leading to reduced compression efficiency. Motivated by the prediction by partial matching context model that has been very successful in text compression, we present prediction by partial approximate matching (PPAM), a method for compression and context modeling for images. Unlike the PPM modeling method that uses exact contexts, PPAM introduces the notion of approximate contexts. Thus, PPAM models the probability of the encoding symbol based on its previous contexts, whereby context occurrences are considered in an approximate manner. The proposed method has competitive compression performance when compared with other popular lossless image compression algorithms. It shows a particularly superior performance when compressing images that have common features, such as biomedical images. View full abstract»

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  • Adaptive Local Linear Regression With Application to Printer Color Management

    Page(s): 936 - 945
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (584 KB) |  | HTML iconHTML  

    Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global ldquooptimalrdquo value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples. View full abstract»

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  • Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

    Page(s): 946 - 957
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5788 KB) |  | HTML iconHTML  

    We propose a wavelet-based codec for the static depth-image-based representation, which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multiple views using a novel rate-distortion (RD) optimization scheme. The rate constraint reduces the ambiguity of depth estimation by favoring piece- wise-smooth depth maps. The optimization is efficiently solved by a novel dynamic programming along trees of integer wavelet coefficients. The codec encodes the image and the depth map jointly to decrease their redundancy and to provide a RD-optimized bitrate allocation between the two. The codec also offers scalability both in resolution and in quality. Experiments on real data show the effectiveness of the proposed codec. View full abstract»

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  • Robust Global Motion Estimation Oriented to Video Object Segmentation

    Page(s): 958 - 967
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2020 KB) |  | HTML iconHTML  

    Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods. View full abstract»

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  • An Efficient and Accurate Method for the Relaxation of Multiview Registration Error

    Page(s): 968 - 981
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1082 KB) |  | HTML iconHTML  

    This paper presents a new method for the relaxation of multiview registration error. The multiview registration problem is represented using a graph. Each node and each edge in the graph represents a 3-D data set and a pairwise registration, respectively. Assuming that all the pairwise registration processes have converged to fine results, this paper shows that the multiview registration problem can be converted into a quadratic programming problem of Lie algebra parameters. The constraints are obtained from every cycle of the graph to eliminate the accumulation errors of global registration. A linear solution is proposed to distribute the accumulation error to proper positions in the graph, as specified by the quadratic model. Since the proposed method does not involve the original 3-D data, it has low time and space complexity. Additionally, the proposed method can be embedded into a trust-region algorithm and, thus, can correctly handle the nonlinear effects of large accumulation errors, while preserving the global convergence property to the first-order critical point. Experimental results confirm both the efficiency and the accuracy of the proposed method. View full abstract»

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  • Estimation of Multiple, Time-Varying Motions Using Time-Frequency Representations and Moving-Objects Segmentation

    Page(s): 982 - 990
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2571 KB) |  | HTML iconHTML  

    We extend existing spatiotemporal approaches to handle time-varying motions estimation of multiple objects. It is shown that multiple, time-varying motions estimation is equivalent to the instantaneous frequency estimation of superpoliteness FM sinusoids. Therefore, we apply established signal processing tools, such as time-frequency representations to show that for each time instant, the energy is concentrated along planes in the 3-D space: spatial frequencies - instantaneous frequency. Using fuzzy C-planes, we estimate indirectly the instantaneous velocities. Furthermore, adapting existing approaches to our problem, we attain the identification of the moving objects. The experimental results verify the effectiveness of our methodology. View full abstract»

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  • New Classes of Radiometric and Combined Radiometric-Geometric Invariant Descriptors

    Page(s): 991 - 1006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4446 KB) |  | HTML iconHTML  

    Real images can contain geometric distortions as well as photometric degradations. Analysis and characterization of those images without recourse to either restoration or geometric standardization is of great importance for the computer vision community as those two processes are often ill-posed problems. To this end, it is necessary to implement image descriptors that make it possible to identify the original image in a simple way independently of the imaging system and imaging conditions. Ideally, descriptors that capture image characteristics must be invariant to the whole range of geometric distortions and photometric degradations, such as blur, that may affect the image. In this paper, we introduce two new classes of radiometric and/or geometric invariant descriptors. The first class contains two types of radiometric invariant descriptors. The first of these type is based on the Mellin transform and the second one is based on central moments. Both descriptors are invariant to contrast changes and to convolution with any kernel having a symmetric form with respect to the diagonals. The second class contains two subclasses of combined invariant descriptors. The first subclass includes central-moment-based descriptors invariant simultaneously to horizontal and vertical translations, to uniform and anisotropic scaling, to stretching, to convolution, and to contrast changes. The second subclass contains central-complex-moment-based descriptors that are simultaneously invariant to similarity transformation and to contrast changes. We apply these invariant descriptors to the matching of geometric transformed and/or blurred images. Experimental results confirm both the robustness and the effectiveness of the proposed invariants. View full abstract»

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  • Principal Axes Estimation Using the Vibration Modes of Physics-Based Deformable Models

    Page(s): 1007 - 1019
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (931 KB) |  | HTML iconHTML  

    This paper addresses the issue of accurate, effective, computationally efficient, fast, and fully automated 2-D object orientation and scaling factor estimation. The object orientation is calculated using object principal axes estimation. The approach relies on the object's frequency-based features. The frequency-based features used by the proposed technique are extracted by a 2-D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations. View full abstract»

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  • Cross-Layer Optimization for Video Transmission Over Multirate GMC-CDMA Wireless Links

    Page(s): 1020 - 1024
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (307 KB) |  | HTML iconHTML  

    In this paper, we consider the problem of video transmission over wireless generalized multicarrier code division multiple access (GMC-CDMA) systems. Such systems offer deterministic elimination of multiple access interference. A scalable video source codec is used and a multirate setup is assumed, i.e., each video user is allowed to occupy more than one GMC-CDMA channels. Furthermore, each of these channels can utilize a different number of subcarriers. We propose a cross-layer optimization method to select the source coding rate, channel coding rate, number of subcarriers per GMC-CDMA channel and transmission power per GMC-CDMA channel given a maximum transmission power for each video user and an available chip rate. Universal rate distortion characteristics (URDC) are used to approximate the expected distortion at the receiver. The proposed algorithm is optimal in the operational rate distortion sense, subject to the specific setup used and the approximation caused by the use of the URDC. Experimental results are presented and conclusions are drawn. View full abstract»

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  • IEEE Transactions on Image Processing EDICS

    Page(s): 1025
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    Freely Available from IEEE
  • IEEE Transactions on Image Processing Information for authors

    Page(s): 1026 - 1027
    Save to Project icon | Request Permissions | PDF file iconPDF (47 KB)  
    Freely Available from IEEE
  • 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

    Page(s): 1028
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    Freely Available from IEEE
  • Leading the field since 1884 [advertisement]

    Page(s): 1029
    Save to Project icon | Request Permissions | PDF file iconPDF (223 KB)  
    Freely Available from IEEE
  • IEEE copyright form

    Page(s): 1030 - 1031
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    Freely Available from IEEE
  • Order form for reprints

    Page(s): 1032
    Save to Project icon | Request Permissions | PDF file iconPDF (353 KB)  
    Freely Available from IEEE
  • IEEE Signal Processing Society Information

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (33 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.

Full Aims & Scope

Meet Our Editors

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