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Image Processing, IEEE Transactions on

Issue 12 • Date Dec. 2006

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Displaying Results 1 - 25 of 26
  • 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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  • From the Editor-in-Chief

    Page(s): 3625 - 3626
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  • Progressive Transmission of Images Over Fading Channels Using Rate-Compatible LDPC Codes

    Page(s): 3627 - 3635
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (599 KB) |  | HTML iconHTML  

    In this paper, we propose a combined source/channel coding scheme for transmission of images over fading channels. The proposed scheme employs rate-compatible low-density parity-check codes along with embedded image coders such as JPEG2000 and set partitioning in hierarchical trees (SPIHT). The assignment of channel coding rates to source packets is performed by a fast trellis-based algorithm. We examine the performance of the proposed scheme over correlated and uncorrelated Rayleigh flat-fading channels with and without side information. Simulation results for the expected peak signal-to-noise ratio of reconstructed images, which are within 1 dB of the capacity upper bound over a wide range of channel signal-to-noise ratios, show considerable improvement compared to existing results under similar conditions. We also study the sensitivity of the proposed scheme in the presence of channel estimation error at the transmitter and demonstrate that under most conditions our scheme is more robust compared to existing schemes View full abstract»

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  • Fuzzy Rank LUM Filters

    Page(s): 3636 - 3654
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    The rank information of samples is widely utilized in nonlinear signal processing algorithms. Recently developed fuzzy transformation theory introduces the concept of fuzzy ranks, which incorporates sample spread (or sample diversity) information into the sample ranking framework. Thus, the fuzzy rank reflects a sample's rank, as well as its similarity to the other sample (namely, joint rank order and spread), and can be utilized to improve the performance of the conventional rank-order-based filters. In this paper, the well-known lower-upper-middle (LUM) filters are generalized utilizing the fuzzy ranks, yielding the class of fuzzy rank LUM (F-LUM) filters. Statistical and deterministic properties of the F-LUM filters are derived, showing that the F-LUM smoothers have similar impulsive noise removal capability to the LUM smoothers, while preserving the image details better. The F-LUM sharpeners are capable of enhancing strong edges while simultaneously preserving small variations. The performance of the F-LUM filters are evaluated for the problems of image impulsive noise removal, sharpening and edge-detection preprocessing. The experimental results show that the F-LUM smoothers can achieve a better tradeoff between noise removal and detail preservation than the LUM smoothers. The F-LUM sharpeners are capable of sharpening the image edges without amplifying the noise or distorting the fine details. The joint smoothing and sharpening operation of the general F-LUM filters also showed superiority in edge detection preprocessing application. In conclusion, the simplicity and versatility of the F-LUM filters and their advantages over the conventional LUM filters are desirable in many practical applications. This also shows that utilizing fuzzy ranks in filter generalization is a promising methodology View full abstract»

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  • Multiscale Hybrid Linear Models for Lossy Image Representation

    Page(s): 3655 - 3671
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5837 KB) |  | HTML iconHTML  

    In this paper, we introduce a simple and efficient representation for natural images. We view an image (in either the spatial domain or the wavelet domain) as a collection of vectors in a high-dimensional space. We then fit a piece-wise linear model (i.e., a union of affine subspaces) to the vectors at each downsampling scale. We call this a multiscale hybrid linear model for the image. The model can be effectively estimated via a new algebraic method known as generalized principal component analysis (GPCA). The hybrid and hierarchical structure of this model allows us to effectively extract and exploit multimodal correlations among the imagery data at different scales. It conceptually and computationally remedies limitations of many existing image representation methods that are based on either a fixed linear transformation (e.g., DCT, wavelets), or an adaptive uni-modal linear transformation (e.g., PCA), or a multimodal model that uses only cluster means (e.g., VQ). We will justify both quantitatively and experimentally why and how such a simple multiscale hybrid model is able to reduce simultaneously the model complexity and computational cost. Despite a small overhead of the model, our careful and extensive experimental results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal-to-noise ratios than many existing methods, including wavelets. We also briefly address how the same (hybrid linear) modeling paradigm can be extended to be potentially useful for other applications, such as image segmentation View full abstract»

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  • Improvement of DCT-Based Compression Algorithms Using Poisson's Equation

    Page(s): 3672 - 3689
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1804 KB) |  | HTML iconHTML  

    We propose two new image compression-decompression methods that reproduce images with better visual fidelity, less blocking artifacts, and better PSNR, particularly in low bit rates, than those processed by the JPEG Baseline method at the same bit rates. The additional computational cost is small, i.e., linearly proportional to the number of pixels in an input image. The first method, the "full mode" polyharmonic local cosine transform (PHLCT), modifies the encoder and decoder parts of the JPEG Baseline method. The goal of the full mode PHLCT is to reduce the code size in the encoding part and reduce the blocking artifacts in the decoder part. The second one, the "partial mode" PHLCT (or PPHLCT for short), modifies only the decoder part, and consequently, accepts the JPEG files, yet decompresses them with higher quality with less blocking artifacts. The key idea behind these algorithms is a decomposition of each image block into a polyharmonic component and a residual. The polyharmonic component in this paper is an approximate solution to Poisson's equation with the Neumann boundary condition, which means that it is a smooth predictor of the original image block only using the image gradient information across the block boundary. Thus, the residual-obtained by removing the polyharmonic component from the original image block-has approximately zero gradient across the block boundary, which gives rise to the fast-decaying DCT coefficients, which, in turn, lead to more efficient compression-decompression algorithms for the same bit rates. We show that the polyharmonic component of each block can be estimated solely by the first column and row of the DCT coefficient matrix of that block and those of its adjacent blocks and can predict an original image data better than some of the other AC prediction methods previously proposed. Our numerical experiments objectively and subjectively demonstrate the superiority of PHLCT over the JPEG Baseline method and the improvement - - of the JPEG-compressed images when decompressed by PPHLCT View full abstract»

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  • Analysis of Superimposed Oriented Patterns

    Page(s): 3690 - 3700
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3580 KB) |  | HTML iconHTML  

    Estimation of local orientation in images may be posed as the problem of finding the minimum gray-level variance axis in a local neighborhood. In bivariate images, the solution is given by the eigenvector corresponding to the smaller eigenvalue of a 2times2 tensor. For an ideal single orientation, the tensor is rank-deficient, i.e., the smaller eigenvalue vanishes. A large minimal eigenvalue signals the presence of more than one local orientation, what may be caused by non-opaque additive or opaque occluding objects, crossings, bifurcations, or corners. We describe a framework for estimating such superimposed orientations. Our analysis is based on the eigensystem analysis of suitably extended tensors for both additive and occluding superpositions. Unlike in the single-orientation case, the eigensystem analysis does not directly yield the orientations, rather, it provides so-called mixed-orientation parameters (MOPs). We, therefore, show how to decompose the MOPs into the individual orientations. We also show how to use tensor invariants to increase efficiency, and derive a new feature for describing local neighborhoods which is invariant to rigid transformations. Applications are, e.g., in texture analysis, directional filtering and interpolation, feature extraction for corners and crossings, tracking, and signal separation View full abstract»

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  • 3-D Discrete Analytical Ridgelet Transform

    Page(s): 3701 - 3714
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8871 KB) |  | HTML iconHTML  

    In this paper, we propose an implementation of the 3-D Ridgelet transform: the 3-D discrete analytical Ridgelet transform (3-D DART). This transform uses the Fourier strategy for the computation of the associated 3-D discrete Radon transform. The innovative step is the definition of a discrete 3-D transform with the discrete analytical geometry theory by the construction of 3-D discrete analytical lines in the Fourier domain. We propose two types of 3-D discrete lines: 3-D discrete radial lines going through the origin defined from their orthogonal projections and 3-D planes covered with 2-D discrete line segments. These discrete analytical lines have a parameter called arithmetical thickness, allowing us to define a 3-D DART adapted to a specific application. Indeed, the 3-D DART representation is not orthogonal, It is associated with a flexible redundancy factor. The 3-D DART has a very simple forward/inverse algorithm that provides an exact reconstruction without any iterative method. In order to illustrate the potentiality of this new discrete transform, we apply the 3-D DART and its extension to the Local-DART (with smooth windowing) to the denoising of 3-D image and color video. These experimental results show that the simple thresholding of the 3-D DART coefficients is efficient View full abstract»

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  • Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation

    Page(s): 3715 - 3727
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    Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods View full abstract»

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  • Efficient Huber-Markov Edge-Preserving Image Restoration

    Page(s): 3728 - 3735
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1639 KB) |  | HTML iconHTML  

    The regularization of the least-squares criterion is an effective approach in image restoration to reduce noise amplification. To avoid the smoothing of edges, edge-preserving regularization using a Gaussian Markov random field (GMRF) model is often used to allow realistic edge modeling and provide stable maximum a posteriori (MAP) solutions. However, this approach is computationally demanding because the introduction of a non-Gaussian image prior makes the restoration problem shift-variant. In this case, a direct solution using fast Fourier transforms (FFTs) is not possible, even when the blurring is shift-invariant. We consider a class of edge-preserving GMRF functions that are convex and have nonquadratic regions that impose less smoothing on edges. We propose a decomposition-enabled edge-preserving image restoration algorithm for maximizing the likelihood function. By decomposing the problem into two subproblems, with one shift-invariant and the other shift-variant, our algorithm exploits the sparsity of edges to define an FFT-based iteration that requires few iterations and is guaranteed to converge to the MAP estimate View full abstract»

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  • Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

    Page(s): 3736 - 3745
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3758 KB) |  | HTML iconHTML  

    We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods View full abstract»

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  • Generating Stochastic Dispersed and Periodic Clustered Textures Using a Composite Hybrid Screen

    Page(s): 3746 - 3758
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    In electrophotographic printing, a periodic clustered-dot halftone pattern is preferred for a smooth and stable result. In addition, the screen frequency should be high enough to minimize the visibility of the halftone textures and to ensure good detail rendition. However, at these frequencies, the halftone cell may contain too few pixels to provide a sufficient number of distinct gray levels. This will result in contouring and posterization. The traditional solution is to grow the clusters asynchronously within a repeating block of clusters known as a supercell. The growth of each individual cluster is governed by a microscreen. The order in which the clusters grow within the supercell is determined by a macroscreen. Typically, the macroscreen is a recursive pattern due to Bayer. In highlights and shadows, this ordering results in visible artifacts. Replacing the Bayer screen by a stochastic macroscreen eliminates these artifacts, but results in new artifacts. In this paper, we propose a new composite screen architecture that employs multiple microscreens and multiple macroscreens in the highlights and shadows. These screens are jointly designed by using the direct binary search (DBS) algorithm View full abstract»

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  • Automatic Object Extraction Over Multiscale Edge Field for Multimedia Retrieval

    Page(s): 3759 - 3772
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    In this work, we focus on automatic extraction of object boundaries from Canny edge field for the purpose of content-based indexing and retrieval over image and video databases. A multiscale approach is adopted where each successive scale provides further simplification of the image by removing more details, such as texture and noise, while keeping major edges. At each stage of the simplification, edges are extracted from the image and gathered in a scale-map, over which a perceptual subsegment analysis is performed in order to extract true object boundaries. The analysis is mainly motivated by Gestalt laws and our experimental results suggest a promising performance for main objects extraction, even for images with crowded textural edges and objects with color, texture, and illumination variations. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed object extraction method and subsequent shape description in the context of multimedia indexing and retrieval. A promising retrieval performance is achieved, and especially in some particular examples, the experimental results show that the proposed method presents such a retrieval performance that cannot be achieved by using other features such as color or texture View full abstract»

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  • Multiple Exemplar-Based Facial Image Retrieval Using Independent Component Analysis

    Page(s): 3773 - 3783
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1957 KB) |  | HTML iconHTML  

    In this paper, we design a content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. This scheme is exploited for developing a multiple example-based retrieval engine. We have explored the use of machine learning techniques for generating the most appropriate feature combination scheme for a given class of images. The combination scheme can be used for developing purposive query engines for specialized image databases. Here, we have considered facial image databases. The effectiveness of the image retrieval system is experimentally demonstrated on different databases View full abstract»

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  • Rotation Moment Invariants for Recognition of Symmetric Objects

    Page(s): 3784 - 3790
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    In this paper, a new set of moment invariants with respect to rotation, translation, and scaling suitable for recognition of objects having N-fold rotation symmetry are presented. Moment invariants described earlier cannot be used for this purpose because most moments of symmetric objects vanish. The invariants proposed here are based on complex moments. Their independence and completeness are proven theoretically and their performance is demonstrated by experiments View full abstract»

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  • Layered Wyner–Ziv Video Coding

    Page(s): 3791 - 3803
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    Following recent theoretical works on successive Wyner-Ziv coding (WZC), we propose a practical layered Wyner-Ziv video coder using the DCT, nested scalar quantization, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information at the decoder). Our main novelty is to use the base layer of a standard scalable video coder (e.g., MPEG-4/H.26L FGS or H.263+) as the decoder side information and perform layered WZC for quality enhancement. Similar to FGS coding, there is no performance difference between layered and monolithic WZC when the enhancement bitstream is generated in our proposed coder. Using an H.26L coded version as the base layer, experiments indicate that WZC gives slightly worse performance than FGS coding when the channel (for both the base and enhancement layers) is noiseless. However, when the channel is noisy, extensive simulations of video transmission over wireless networks conforming to the CDMA2000 1X standard show that H.26L base layer coding plus Wyner-Ziv enhancement layer coding are more robust against channel errors than H.26L FGS coding. These results demonstrate that layered Wyner-Ziv video coding is a promising new technique for video streaming over wireless networks View full abstract»

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  • Use of Fresnelets for Phase-Shifting Digital Hologram Compression

    Page(s): 3804 - 3811
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    Fresnelets are wavelet-like base functions specially tailored for digital holography applications. We introduce their use in phase-shifting interferometry (PSI) digital holography for the compression of such holographic data. Two compression methods are investigated. One uses uniform quantization of the Fresnelet coefficients followed by lossless coding, and the other uses set portioning in hierarchical trees (SPIHT) coding. Quantization and lossless coding of the original data is used to compare the performance of the proposed algorithms. The comparison reveals that the Fresnelet transform of phase-shifting holograms in combination with SPIHT or uniform quantization can be used very effectively for the compression of holographic data. The performance of the new compression schemes is demonstrated on real PSI digital holographic data View full abstract»

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  • Video Compression Using Spatiotemporal Regularity Flow

    Page(s): 3812 - 3823
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    We propose a new framework in wavelet video coding to improve the compression rate by exploiting the spatiotemporal regularity of the data. A sequence of images creates a spatiotemporal volume. This volume is said to be regular along the directions in which the pixels vary the least, hence the entropy is the lowest. The wavelet decomposition of regularized data results in a fewer number of significant coefficients, thus yielding a higher compression rate. The directions of regularity of an image sequence depend on both its motion content and spatial structure. We propose the representation of these directions by a 3-D vector field, which we refer to as the spatiotemporal regularity flow (SPREF). SPREF uses splines to approximate the directions of regularity. The compactness of the spline representation results in a low storage overhead for SPREF, which is a desired property in compression applications. Once SPREF directions are known, they can be converted into actual paths along which the data is regular. Directional decomposition of the data along these paths can be further improved by using a special class of wavelet basis called the 3-D orthonormal bandelet basis. SPREF -based video compression not only removes the temporal redundancy, but it also compensates for the spatial redundancy. Our experiments on several standard video sequences demonstrate that the proposed method results in higher compression rates as compared to the standard wavelet based compression View full abstract»

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  • Feature Extraction Using Recursive Cluster-Based Linear Discriminant With Application to Face Recognition

    Page(s): 3824 - 3832
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB) |  | HTML iconHTML  

    A novel recursive procedure for extracting discriminant features, termed recursive cluster-based linear discriminant (RCLD), is proposed in this paper. Compared to the traditional Fisher linear discriminant (FLD) and its variations, RCLD has a number of advantages. First of all, it relaxes the constraint on the total number of features that can be extracted. Second, it fully exploits all information available for discrimination. In addition, RCLD is able to cope with multimodal distributions, which overcomes an inherent problem of conventional FLDs, which assumes uni-modal class distributions. Extensive experiments have been carried out on various types of face recognition problems for Yale, Olivetti Research Laboratory, and JAFFE databases to evaluate and compare the performance of the proposed algorithm with other feature extraction methods. The resulting improvement of performances by the new feature extraction scheme is significant View full abstract»

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  • List of reviewers

    Page(s): 3833 - 3838
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  • IEEE Transactions on Image Processing Edics

    Page(s): 3839
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  • IEEE Transactions on Image Processing Information for authors

    Page(s): 3840 - 3841
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  • IEEE Transactions on Multimedia

    Page(s): 3842
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  • 2006 Index

    Page(s): 3843 - 3872
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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