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Image Processing, IEEE Transactions on

Issue 7 • Date Jul 1995

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Displaying Results 1 - 17 of 17
  • Adaptive postprocessing algorithms for low bit rate video signals

    Page(s): 1032 - 1035
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    A new adaptive postprocessing algorithm to enhance the quality of a noisy video sequence is presented. The algorithm recognizes that the visibility of noise depends on local signal characteristics. It therefore classifies the video signal into different classes and uses separate nonlinear filters matched to each class. The most general version of the algorithm employs motion-compensated frame averaging to improve picture quality in a first stage. A classification algorithm subsequently divides subblocks of pixels in the averaged frame into four classes: edge, smooth, nonsmooth with motion and nonsmooth without motion. Spatial algorithms that perform multilevel median filtering, double median filtering, and median filtering are used for pixels belonging to edge, smooth, and nonsmooth with motion categories. Pixels in the nonsmooth, unmoving category are left unfiltered to preserve corresponding image texture. In a simpler version of this four-class system, the motion cues and motion-compensated frame averaging are eliminated, and the purely spatial filtering is based on a three-class algorithm. When used at the output of a 3-D subband coder at 384 kbps, the spatial postfilter was shown to provide a consistent gain in subjectively evaluated picture quality. Twenty-five viewers participated in an experiment involving three coded sequences. In a pairwise comparison of postfiltered and unfiltered sequences, the postfiltered version was judged to be better in 63 out of 75 instances View full abstract»

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  • Morphological pyramids with alternating sequential filters

    Page(s): 965 - 977
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    The aim of this paper is to find a relationship between alternating sequential filters (ASF) and the morphological sampling theorem (MST) developed by Haralick et al. (1987). The motivation behind this approach is to take advantage of the computational efficiency offered by the MST to implement morphological operations. First, we show alternative proofs for opening and closing in the sampled and unsampled domain using the basis functions. These proofs are important because they show that it possible to obtain any level of a morphological pyramid in one step rather than the traditional two-step procedure. This decomposition is then used to show the relationship of the open-closing in the sampled and unsampled domain. An upper and a lower bound, for the above relationships, are presented. Under certain circumstances, an equivalence is shown for open-closing between the sampled and the unsampled domain. An extension to more complicated algorithms using a union of openings and an intersection of closings is also proposed. Using the Hausdorff metric, it is shown that a morphologically reconstructed image cannot have a better accuracy than twice the radius of the reconstruction structuring element. Binary and gray scale examples are presented View full abstract»

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  • Automatic target segmentation by locally adaptive image thresholding

    Page(s): 1036 - 1041
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (540 KB)  

    A locally adaptive thresholding algorithm, concerning the extraction of targets from a given field of background, is proposed. Conventional histogram-based or global-type methods are deficient in detecting small targets of possibly low contrast as well. The present research is notable for solving the mentioned problems by introducing (1) shape connectivity measure based on co-occurrence statistics for threshold evaluation; and (2) no-target identification procedure for modeling a local-processing paradigm. In this manner, thresholds are determined adaptively even in the presence of space-varying noise or clutter. Experiments show that the results are reliable and even outperform those that manual operations can achieve for global thresholding View full abstract»

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  • Real-time synthetic aperture sonar imaging using a parallel architecture

    Page(s): 1010 - 1019
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    This paper describes a parallel architecture that has been developed to perform real-time synthetic aperture sonar imaging as part of the Acoustical Imaging Development (ACID) project. The project has successfully developed a synthetic aperture sonar system for producing high resolution images of the sea floor and that has been tested during a series of sea trials in May 1993 off the south coast of France. This paper describes the synthetic aperture processing system developed by the University of Newcastle upon Tyne and its use of transputer modules and associated devices in order to obtain real-time imaging performance, the software structure of the processing system and the load balancing techniques that have been developed in order to provide efficient processing. The use of a parallel distributed architecture has also allowed a processing system that can readily be extended to deliver greater computational power in the future. Images produced by the synthetic aperture processor from data collected from around the Toulon coastal region are presented. These images highlight the improvement in azimuth resolution that can be obtained from synthetic aperture processing over conventional sidescan sonars View full abstract»

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  • The generalized Gabor transform

    Page(s): 978 - 988
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    The generalized Gabor transform (for image representation) is discussed. For a given function f(t), t∈R, the generalized Gabor transform finds a set of coefficients amr such that f(t)=Σm=-∞Σ r=-∞αmr g(t-mT)exp(i2πrt/T'). The original Gabor transform proposed by D. Gabor (1946) is the special case of T=T'. The computation of the generalized Gabor transform with biorthogonal functions is discussed. The optimal biorthogonal functions are discussed. A relation between a window function and its optimal biorthogonal function is presented based on the Zak (1967) transform when T/T' is rational. The finite discrete generalized Gabor transform is also derived. Methods of computation for the biorthogonal function are discussed. The relation between a window function and its optimal biorthogonal function derived for the continuous variable generalized Gabor transform can be extended to the finite discrete case. Efficient algorithms for the optimal biorthogonal function and generalized Gabor transform for the finite discrete case are proposed View full abstract»

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  • Bispectral analysis and model validation of texture images

    Page(s): 996 - 1009
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    Statistical approaches to texture analysis and synthesis have largely relied upon random models that characterize the 2-D process in terms of its first- and second-order statistics, and therefore cannot completely capture phase properties of random fields that are non-Gaussian and/or asymmetric. In this paper, higher than second-order statistics are used to derive and implement 2-D Gaussianity, linearity, and spatial reversibility tests that validate the respective modeling assumptions. The nonredundant region of the 2-D bispectrum is correctly defined and proven. A consistent parameter estimator for nonminimum phase, asymmetric noncausal, 2-D ARMA models is derived by minimizing a quadratic error polyspectrum matching criterion. Simulations on synthetic data are performed and the results of the bispectral analysis on real textures are reported View full abstract»

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  • Comparison between adaptive search and bit allocation algorithms for image compression using vector quantization

    Page(s): 1020 - 1023
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    This article discusses bit allocation and adaptive search algorithms for mean-residual vector quantization (MRVQ) and multistage vector quantization (MSVQ). The adaptive search algorithm uses a buffer and a distortion threshold function to control the bit rate that is assigned to each input vector. It achieves a constant rate for the entire image but variable bit rate for each vector in the image. For a given codebook and several bit rates, we compare the performance between the optimal bit allocation and adaptive search algorithms. The results show that the performance of the adaptive search algorithm is only 0.20-0.53 dB worse than that of the optimal bit allocation algorithm, but the complexity of the adaptive search algorithm is much less than that of the optimal bit allocation algorithm View full abstract»

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  • Optimal Gabor filters for texture segmentation

    Page(s): 947 - 964
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1920 KB)  

    Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the Gabor filter parameters are suitably chosen. Some previous analysis has shown how to design filters for discriminating simple textures. Designing filters for more general natural textures, though, has largely been done ad hoc. We have devised a more rigorously based method for designing Gabor filters. It assumes that an image contains two different textures and that prototype samples of the textures are given a priori. We argue that Gabor filter outputs can be modeled as Rician random variables (often approximated well as Gaussian rv's) and develop a decision-theoretic algorithm for selecting optimal filter parameters. To improve segmentations for difficult texture pairs, we also propose a multiple-filter segmentation scheme, motivated by the Rician model. Experimental results indicate that our method is superior to previous methods in providing useful Gabor filters for a wide range of texture pairs View full abstract»

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  • A domain operator for binary morphological processing

    Page(s): 1042 - 1046
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB)  

    A new class of morphological operators, the domain operator, is proposed for binary images. The operation is defined with a domain function based on the index function of fuzzy sets. With appropriate definition of the connection weights, the domain operator can implement conventional morphological operations such as dilation, erosion, opening, and closing, as well as other image processing functions such as noise reduction, edge detection, and clump splitting View full abstract»

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  • Image coding by block prediction of multiresolution subimages

    Page(s): 909 - 920
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    The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG View full abstract»

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  • Optimal median-type filtering under structural constraints

    Page(s): 921 - 931
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    This paper presents a method for the design of median-type filters that achieve the maximum noise attenuation under structural constraints imposed by the requirement of preserving certain signal or image features. As compared with the design of optimal weighted median (WM) filters that calls for solving a set of linear inequalities, this method is extremely simple yet general enough. The filter is obtained by modifying directly the Boolean function of a median filter, and an analytic, closed-form representation of its Boolean function can be obtained. Furthermore, it is proven theoretically that under the same set of structural constraints, the filter designed in this way will never do worse in removing i.i.d. noise with any distribution than the optimal WM filter-improvements as high as 41, 46, and 52% have been achieved in simulations in a 2-D case for the uniform, Gaussian, and double-exponential distributions, respectively. Also, the improvement in the impulsive noise environment is very significant, as is demonstrated by an image enhancement application View full abstract»

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  • Nonlinear image recovery with half-quadratic regularization

    Page(s): 932 - 946
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    One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function that enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate but are often far less accessible. This is particularly true when the objective function is nonconvex, and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function so that the original image estimate can be obtained by joint minimization. This can be done efficiently by Monte Carlo methods, for example by FFT-based annealing using a Markov chain that alternates between (global) transitions from one array to the other. Experiments are reported in optical astronomy, with space telescope data, and computed tomography View full abstract»

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  • On some Bayesian/regularization methods for image restoration

    Page(s): 989 - 995
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    Methods are reviewed for choosing regularized restorations in image processing. In particular, a method developed by Galatsanos and Katsaggelos (see ibid., vol.1, p.322-336, 1992) is given a Bayesian interpretation and is compared with other Bayesian and non-Bayesian alternatives. A small illustrative example is provided and a complement is provided for the discussion of noise variance estimation of Galatsanos et al View full abstract»

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  • Recursive soft morphological filters

    Page(s): 1027 - 1032
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    We present properties of recursive soft morphological filters that use previously filtered outputs as their inputs, cascade combinations of these filters, and the idempotent recursive soft morphological filters. The development allows problems in the implementation of cascaded recursive soft morphological filters to be reduced to the equivalent problems of a single recursive standard morphological filter View full abstract»

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  • Transform coded image reconstruction exploiting interblock correlation

    Page(s): 1023 - 1027
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (652 KB)  

    Transmission of still images and video over lossy packet networks presents a reconstruction problem at the decoder. Specifically, in the case of block-based transform coded images, loss of one or more packets due to network congestion or transmission errors can result in errant or entirely lost blocks in the decoded image. This article proposes a computationally efficient technique for reconstruction of lost transform coefficients at the decoder that takes advantage of the correlation between transformed blocks of the image. Lost coefficients are linearly interpolated from the same coefficients in adjacent blocks subject to a squared edge error criterion, and the resulting reconstructed coefficients minimize blocking artifacts in the image while providing visually pleasing reconstructions. The required computational expense at the decoder per reconstructed block is less than 1.2 times a non-recursive DCT, and as such this technique is useful for low power, low complexity applications that require good visual performance View full abstract»

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  • Projection-based spatially adaptive reconstruction of block-transform compressed images

    Page(s): 896 - 908
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    At the present time, block-transform coding is probably the most popular approach for image compression. For this approach, the compressed images are decoded using only the transmitted transform data. We formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. Numerical experiments are shown that demonstrate that the proposed algorithms work better than both the JPEG deblocking recommendation and our previous projection-based image decoding approach View full abstract»

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  • Recursive time-varying filter banks for subband image coding

    Page(s): 885 - 895
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    Filter banks, subband/wavelets, and multiresolution decompositions that employ recursive filters have been considered previously and are recognized for their efficiency in partitioning the frequency spectrum. This paper presents an analysis of a new infinite impulse response (IIR) filter bank in which these computationally efficient filters may be changed adaptively in response to the input. The new filter bank framework is presented and discussed in the context of subband image coding. In the absence of quantization errors, exact reconstruction can be achieved. By the proper choice of an adaptation scheme, it is shown that recursive linear time-varying (LTV) filter banks can yield improvement over conventional ones 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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University of Virginia
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E-mail: acton@virginia.edu 
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