By Topic

Image Processing, IEEE Transactions on

Issue 6 • Date June 1995

Filter Results

Displaying Results 1 - 18 of 18
  • A variational approach to the radiometric enhancement of digital imagery

    Page(s): 845 - 849
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (984 KB)  

    In this correspondence, we present a variational approach to the problem of finding suitable radiometric image transformations that optimize desirable characteristics of the output image histogram. This variational approach can be interpreted as the minimization of the cumulative spacing between histogram bars in the least squares sense subject to some weight function. Most of the common histogram transformation procedures used in remote sensing applications can be deduced from this general variational approach with an appropriate choice of the weight function.<> View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Pruned tree-structured vector quantization of medical images with segmentation and improved prediction

    Page(s): 734 - 742
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (976 KB)  

    The authors use predictive pruned tree-structured vector quantization for the compression of medical images. Their goal is to obtain a high compression ratio without impairing the image quality, at least so far as diagnostic purposes are concerned. The authors use a priori knowledge of the class of images to be encoded to help them segment the images and thereby to reserve bits for diagnostically relevant areas. Moreover, the authors improve the quality of prediction and encoding in two additional ways: by increasing the memory of the predictor itself and by using ridge regression for prediction. The improved encoding scheme was tested via computer simulations on a set of mediastinal CT scans; results are compared with those obtained using a more conventional scheme proposed recently in the literature. There were remarkable improvements in both the prediction accuracy and the encoding quality, above and beyond what comes from the segmentation. Test images were encoded at 0.5 bit per pixel and less without any visible degradation for the diagnostically relevant region View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Iterative maximum likelihood displacement field estimation in quantum-limited image sequences

    Page(s): 743 - 751
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (812 KB)  

    We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel) View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optimal color filters in the presence of noise

    Page(s): 814 - 823
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (812 KB)  

    The effect of noise on the number of effective channels (color filters) used to record a color image is investigated. Transmittances of color filters are calculated that minimize the mean square error that occurs when estimating, from the recorded data, the colors in the image under a collection of viewing illuminants. Since the results indicate that a significant improvement in color correction accuracy is achieved by using four channels, there is good reason to consider using four-tuples for representation of colorimetric information View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Morphological representation of order-statistics filters

    Page(s): 838 - 845
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1024 KB)  

    We propose a comprehensive theory for the morphological bounds on order-statistics filters (and their repeated iterations). Conditions are derived for morphological openings and closings to serve as bounds (lower and upper, respectively) on order-statistics filters (and their repeated iterations). Under various assumptions, morphological open-closings and close-openings are also shown to serve as (tighter) bounds (lower and upper, respectively) on iterations of order-statistics filters. Simulations of the application of the results presented to image restoration are finally provided View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Boundary localization in texture segmentation

    Page(s): 849 - 856
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1228 KB)  

    Localizing boundaries between textured image regions without sacrificing the labeling accuracy of interior regions remains a problem in segmentation. Difficulties arise because of the conflicting requirements of localization and labeling. Boundary localization usually demands observing the features over small neighborhoods, whereas labeling accuracy increases with the size of the observation neighborhood. This problem is further exacerbated in texture segmentation by the spatially distributed nature of texture features. In this correspondence, we develop a multiresolution approach that combines localized and distributed features to directly address boundary localization in texture segmentation. Maximum localization is achieved by using the gray-level discontinuities at the boundary between textures to define the boundary. The properties that characterize the gray-level discontinuity at texture boundaries are developed and an algorithm is designed to localize the boundary using these discontinuities. This segmentation algorithm is implemented and successfully tested on a set of Brodatz texture mosaics and AVHRR satellite imagery View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multiple-cost constraints for the design of tree-structured vector quantizers

    Page(s): 824 - 828
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB)  

    Minimizing the distortion subject to a cost constraint is fundamental in the design of tree-structured vector quantizers. Because of various competing cost measures, the use of single-cost constraints has led to undesirable results. The author studies the relationships among several cost functions and shows how multiple-cost constraints can be used to significantly improve tree design View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unsupervised texture segmentation of images using tuned matched Gabor filters

    Page(s): 863 - 870
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (912 KB)  

    Recent studies have confirmed that the multichannel Gabor decomposition represents an excellent tool for image segmentation and boundary detection. Unfortunately, this approach when used for unsupervised image analysis tasks imposes excessive storage requirements due to the nonorthogonality of the basis functions and is computationally highly demanding. In this correspondence, we propose a novel method for efficient image analysis that uses tuned matched Gabor filters. The algorithmic determination of the parameters of the Gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal Gabor transforms with progressive dyadic decrease of elementary cell sizes. The method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised. Computer simulations applied to different classes of textures illustrate the matching property of the tuned Gabor filters derived using our determination algorithm. Also, their capability to extract significant image information and thus enable an easy and efficient low-level image analysis will be demonstrated View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A class of robust entropic functionals for image restoration

    Page(s): 752 - 773
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2596 KB)  

    This paper considers the concept of robust estimation in regularized image restoration. Robust functionals are employed for the representation of both the noise and the signal statistics. Such functionals allow the efficient suppression of a wide variety of noise processes and permit the reconstruction of sharper edges than their quadratic counterparts. A new class of robust entropic functionals is introduced, which operates only on the high-frequency content of the signal and reflects sharp deviations in the signal distribution. This class of functionals can also incorporate prior structural information regarding the original image, in a way similar to the maximum information principle. The convergence properties of robust iterative algorithms are studied for continuously and noncontinuously differentiable functionals. The definition of the robust approach is completed by introducing a method for the optimal selection of the regularization parameter. This method utilizes the structure of robust estimators that lack analytic specification. The properties of robust algorithms are demonstrated through restoration examples in different noise environments View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multiresolution tomographic reconstruction using wavelets

    Page(s): 799 - 813
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1388 KB)  

    Shows how the separable two-dimensional wavelet representation leads naturally to an efficient multiresolution tomographic reconstruction algorithm. This algorithm is similar to the conventional filtered backprojection algorithm, except that the filters are now angle dependent, and the backprojection gives the wavelet coefficients of the reconstruction, which are then used to synthesize the reconstruction at various resolution levels. By reconstructing only a small localized region at high resolution, the authors show how radiation exposure and computation can be significantly reduced, compared to a standard reconstruction View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Extended lapped transform in image coding

    Page(s): 828 - 832
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (828 KB)  

    A modulated lapped transform with extended overlap (ELT) is investigated in image coding with the objective of verifying its potential to replace the discrete cosine transform (DCT) in specific applications. Some of the criteria utilized for the performance comparison are reconstructed image quality (both objective and subjective), reduction of blocking artifacts, robustness against transmission errors, and filtering (for scalability). Also, a fast implementation algorithm for finite-length-signals using symmetric extensions is developed specially for the ELT with overlap factor 2 (ELT-2). This comparison shows that ELT-2 is superior to both DCT and the lapped orthogonal transform (LOT) View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image coding using wavelet transforms and entropy-constrained trellis-coded quantization

    Page(s): 725 - 733
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (876 KB)  

    The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multi-resolution approximations. Multi-resolution decomposition schemes have proven to be very effective for high-quality, low bit-rate image coding. In this work, we investigate the use of entropy-constrained trellis-coded quantization (ECTCQ) for encoding the wavelet coefficients of both monochrome and color images. ECTCQ is known as an effective scheme for quantizing memoryless sources with low to moderate complexity, The ECTCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. Performance comparisons are made using the classical quadrature mirror filter bank of Johnston and nine-tap spline filters that were built from biorthogonal wavelet bases. We conclude that the encoded images obtained from the system employing nine-tap spline filters are marginally superior although at the expense of additional computational burden. Excellent peak-signal-to-noise ratios are obtained for encoding monochrome and color versions of the 512×512 “Lenna” image. Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear multivariate image filtering techniques

    Page(s): 788 - 798
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (960 KB)  

    In this paper, nonlinear multivariate image filtering techniques are proposed to handle color images corrupted by noise. First, we briefly review the principle of reduced ordering (R-ordering) and then define three R-orderings by selecting different central locations. Considering noise attenuation, edge preservation, and detail retention, R-ordering based multivariate filters are designed by combining the R-ordering schemes. To implement color image filtering more effectively, we develop them into a locally adaptive version. The output of the adaptive filter is the closest sample to a central location that is a weighted linear combination of the mean, the marginal median, and the center sample. As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the identity filter. The performance of the two adaptive filtering techniques is compared with that of some nonadaptive ones. The examples of color image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics

    Page(s): 856 - 862
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1012 KB)  

    Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this correspondence, we present an unsupervised texture segmentation method that does not require knowledge about the different texture regions, their parameters, or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmented-state Markov random field, including an outlier class that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world textured images are presented View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A distortion measure for blocking artifacts in images based on human visual sensitivity

    Page(s): 713 - 724
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1104 KB)  

    A visual model that gives a distortion measure for blocking artifacts in images is presented. Given the original and reproduced image as inputs, the model output is a numerical value that quantifies the visibility of blocking error in the reproduced image. The model is derived based on the human visual sensitivity to horizontal and vertical edge artifacts that result from blocking. Psychovisual experiments have been carried out to measure the visual sensitivity to these artifacts. In the experiments, typical edge artifacts are shown to subjects and the sensitivity to them is measured with the variation of background luminance, background activity, edge length, and edge amplitude. Synthetic test patterns are used as background images in the experiments. The sensitivity measures thus obtained are used to estimate the model parameters. The final model is tested on real images, and the results show that the error visibility predicted by the model correlates well with the subjective ranking View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Region-based fractal image compression using heuristic search

    Page(s): 832 - 838
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (916 KB)  

    Presents work carried out on fractal (or attractor) image compression. The approach relies on the assumption that image redundancy can be efficiently exploited through self-transformability. The algorithms described utilize a novel region-based partition of the image that greatly increases the compression ratios achieved over traditional block-based partitionings. Due to the large search spaces involved, heuristic algorithms are used to construct these region-based transformations. Results for three different heuristic algorithms are given. The results show that the region-based system achieves almost double the compression ratio of the simple block-based system at a similar decompressed image quality. For the Lena image, compression ratios of 41:1 can be achieved at a PSNR of 26.56 dB View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A multiresolution approach to computer verification of handwritten signatures

    Page(s): 870 - 874
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB)  

    We took a multi-resolution approach to the signature verification problem. The top-level representation of signatures was the global geometric features. A multi-resolution representation of signatures was obtained using the wavelet transformation. We built VQ and network classifiers to demonstrate the advantages of the multi-resolution approach. High verification rates were achieved based on a limited database View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear scale-space filtering and multiresolution system

    Page(s): 774 - 787
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1280 KB)  

    We derive and demonstrate a nonlinear scale-space filter and its application in generating a nonlinear multiresolution system. For each datum in a signal, a neighborhood of weighted data is used for clustering. The cluster center becomes the filter output. The filter is governed by a single scale parameter that dictates the spatial extent of nearby data used for clustering. This, together with the local characteristic of the signal, determines the scale parameter in the output space, which dictates the influences of these data on the output. This filter is thus adaptive and data driven. It provides a mechanism for (a) removing impulsive noise, (b) improved smoothing of nonimpulsive noise, and (c) preserving edges. Comparisons with Gaussian scale-space filtering and median filters are made using real images. Using the architecture of the Laplacian pyramid and this nonlinear filter for interpolation, we construct a nonlinear multiresolution system that has two features: (1) edges are well preserved at low resolutions, and (2) difference signals are small and spatially localized. This filter implicitly presents a new mechanism for detecting discontinuities differing from techniques based on local gradients and line processes. This work shows that scale-space filtering, nonlinear filtering, and scale-space clustering are closely related and provides a framework within which further image processing, image coding, and computer vision problems can be investigated View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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