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Image Processing, IEEE Transactions on

Issue 3 • Date May 1994

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Displaying Results 1 - 13 of 13
  • Optimal space-varying regularization in iterative image restoration

    Page(s): 319 - 324
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (524 KB)  

    It has been shown that space-variant regularization in image restoration provides better results than space-invariant regularization. However, the optimal choice of the regularization parameter is usually unknown a priori. In previous work, the generalized cross-validation (GCV) criterion was shown to provide accurate estimates of the optimal regularization parameter. The author introduces a modified form of the GCV criterion that incorporates space-variant regularization and data error terms. Furthermore, he presents an efficient method for estimating the GCV criterion for the space-variant case using iterative image restoration techniques. This method performs nearly as well as the exact criterion for the image restoration problem. In addition, he proposes a Wiener filter interpretation for choosing the local weighting of the regularization. This interpretation suggests the use of a multistage estimation procedure to estimate the optimal choice of the local regularization weights. Experiments confirm the value of the modified GCV estimation criterion as well as the multistage procedure for estimating the local regularization weights View full abstract»

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  • Image identification and restoration in the subband domain

    Page(s): 312 - 314
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB)  

    When faced with a large support point spread function (PSF), the iterative expectation-maximization (EM) algorithm, which is often used for PSF identification, is very sensitive to the initial PSF estimate. To deal with this problem, the authors propose to do EM image identification and restoration in the subband domain. After the image is first divided into subbands, the EM algorithm is applied to each subband separately. Since the PSF can be taken to have smaller support in each subband, these subbands should be less of a problem with the EM model identification. They also introduce an adaptive subband EM method for use in the upper frequency subbands View full abstract»

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  • Optimal parallel stack filtering under the mean absolute error criterion

    Page(s): 324 - 327
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block threshold decomposition (BTD) is introduced, and its properties are investigated. The design of optimal PSHs under the mean absolute error (MAE) criterion is shown to be similar to the minimum MAE stack filtering theory. The only difference is that one needs now to design more than one stack filter that together construct an optimal PSF. As a result, while reviewing briefly the optimal stack filtering theory, they will put more efforts to demonstrate, via several examples, the improvement by switching from stack filtering to parallel stack filtering for the task of image noise removal View full abstract»

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  • Hidden Markov models applied to on-line handwritten isolated character recognition

    Page(s): 314 - 318
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (460 KB)  

    Hidden Markov models are used to model the generation of handwritten, isolated characters. Models are trained on examples using the Baum-Welch optimization routine. Then, given the models for the alphabet, unknown characters can be classified using maximum-likelihood classification. Experiments have been conducted, and an average error rate of 6.9% was achieved over the alphabet consisting of the lowercase English alphabet View full abstract»

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  • The Khoros software development environment for image and signal processing

    Page(s): 243 - 252
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (968 KB)  

    Data flow visual language systems allow users to graphically create a block diagram of their applications and interactively control input, output, and system variables. Khoros is an integrated software development environment for information processing and visualization. It is particularly attractive for image processing because of its rich collection of tools for image and digital signal processing. This paper presents a general overview of Khoros with emphasis on its image processing and DSP tools. Various examples are presented and the future direction of Khoros is discussed View full abstract»

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  • Sidelobe reduction via adaptive FIR filtering in SAR imagery

    Page(s): 292 - 301
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (880 KB)  

    The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l1- or l2-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery View full abstract»

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  • Efficient quadtree coding of images and video

    Page(s): 327 - 331
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB)  

    The quadtree data structure is commonly used in image coding to decompose an image into separate spatial regions to adaptively identify the type of quantizer used in various regions of an image. The authors describe the theory needed to construct quadtree data structures that optimally allocate rate, given a set of quantizers. A Lagrange multiplier method finds these optimal rate allocations with no monotonicity restrictions. They use the theory to derive a new quadtree construction method that uses a stepwise search to find the overall optimal quadtree structure. The search can be driven with either actual measured quantizer performance or ensemble average predicted performance. They apply this theory to the design of a motion compensated interframe video coding system using a quadtree with vector quantization View full abstract»

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  • Index assignment for progressive transmission of full-search vector quantization

    Page(s): 307 - 312
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (548 KB)  

    The authors study codeword index assignment to allow for progressive image transmission of fixed rate full-search vector quantization (VQ). They develop three new methods of assigning indices to a vector quantization codebook and formulate these assignments as labels of nodes of a full-search progressive transmission tree. The tree is used to design intermediate codewords for the decoder so that full-search VQ has a successive approximation character. The binary representation for the path through the tree represents the progressive transmission code. The methods of designing the tree that they apply are the generalized Lloyd algorithm, minimum cost perfect matching from optimization theory, and a method of principal component partitioning. Their empirical results show that the final method gives intermediate signal-to-noise ratios (SNRs) that are close to those obtained with tree-structured vector quantization, yet they have higher final SNRs View full abstract»

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  • An algorithm for successive identification of reflections

    Page(s): 281 - 291
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    A new algorithm for successive identification of seismic reflections is proposed. Generally, the algorithm can be viewed as a curve matching method for images with specific structure. However, in the paper, the algorithm works on seismic signals assembled to constitute an image in which the investigated reflections produce curves. In numerical examples, the authors work on signals assembled in CMP gathers. The key idea of the algorithm is to estimate the reflection curve parameters and the reflection coefficients along these curves by combining the multipulse technique and the generalized Radon transform. The multipulse technique is used for wavelet identification in each trace, and the generalized Radon transform is used to coordinate the wavelet identification between the individual traces. Furthermore, a stop criterion and a reflection validation procedure are presented. The stop criterion stops the reflection estimation when the actual estimated reflection is insignificant. The reflection validation procedure ensures that the estimated reflections follow the shape of the investigated reflection curves. The algorithm is successfully used in two numerical examples. One is based on a synthetic CMP gather, whereas the other is based on a real recorded CMP gather. Initially, the algorithm requires an estimate of the wavelet that can be performed by any wavelet estimation method View full abstract»

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  • Space-frequency localized image compression

    Page(s): 302 - 307
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (676 KB)  

    Subband and wavelet-based image compression can be viewed as frequency oriented techniques because each subimage in the decomposition is essentially a band-pass version of the original image. The authors suggest a space-frequency partition scheme to fully exploit the excellent localization properties of wavelets in both the spatial and frequency domains. Due to the relatively large number of blocks in this partition compared with traditional subband coders, the rate required for communicating the quantizer configurations must be taken into account. An iterative bit allocation algorithm is suggested that minimizes the mean square error given the overall rate for specifying the quantization configuration and for quantizing the wavelet coefficients. Images encoded using scalar quantization under this scheme show improvements in PSNR versus rate over traditional subband and wavelet-based methods View full abstract»

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  • A structure for adaptive order statistics filtering

    Page(s): 265 - 280
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1324 KB)  

    In applications such as smoothing and enhancement of images, adaptive filtering techniques offer the flexibility needed for good performance with non-stationary observations. Many adaptive schemes can be based on the idea of determining the local statistics of the signal through appropriate tests on the data, to aid in the selection of a filtering procedure that is suited to the data. In the paper, the authors consider decision-directed or data-dependent adaptive filtering schemes that are based on order statistics. A general formulation for such a class of adaptive order statistics filters is presented. Approximate statistical performance analysis, especially in the presence of edges, may be carried out for this entire class of filters. The authors give examples of some existing filters that fit into this framework. The formulation also accommodates filters that employ multiple windows in their operation. To illustrate the potential of this class of multiple window (MW) filters, they construct and analyze simple filters, like the triple window median (TW-MED) and the triple window median of means (TW-MOM) filters, that are shown to yield useful performance. The class of mean-median hybrid (MMH) filters is also presented as a simple example which may be extended to give interesting performance View full abstract»

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  • A Bayesian approach to image expansion for improved definition

    Page(s): 233 - 242
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (776 KB)  

    Accurate image expansion is important in many areas of image analysis. Common methods of expansion, such as linear and spline techniques, tend to smooth the image data at edge regions. This paper introduces a method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation techniques that are proposed for noise-free and noisy images result in the optimization of convex functionals. The expanded images produced from these methods will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion View full abstract»

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  • Fan filters, the 3-D Radon transform, and image sequence analysis

    Page(s): 253 - 264
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    This paper develops a theory for the application of fan filters to moving objects. In contrast to previous treatments of the subject based on the 3-D Fourier transform, simplicity and insight are achieved by using the 3-D Radon transform. With this point of view, the Radon transform decomposes the image sequence into a set of plane waves that are parameterized by a two-component slowness vector. Fan filtering is equivalent to a multiplication in the Radon transform domain by a slowness response function, followed by an inverse Radon transform. The plane wave representation of a moving object involves only a restricted set of slownesses such that the inner product of the plane wave slowness vector and the moving object velocity vector is equal to one. All of the complexity in the application of fan filters to image sequences results from the velocity-slowness mapping not being one-to-one; therefore, the filter response cannot be independently specified at all velocities. A key contribution of this paper is to elucidate both the power and the limitations of fan filtering in this new application. A potential application of 3-D fan filters is in the detection of moving targets in clutter and noise. For example, an appropriately designed fan filter can reject perfectly all moving objects whose speed, irrespective of heading, is less than a specified cut-off speed, with only minor attenuation of significantly faster objects. A simple geometric construction determines the response of the filter for speeds greater than the cut-off speed View full abstract»

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