Publications (44)48.34 Total impact
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Article: Fuzzy Random Impulse Noise Removal From Color Image Sequences
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ABSTRACT: In this paper, a new fuzzy filter for the removal of random impulse noise in color video is presented. By working with different successive filtering steps, a very good tradeoff between detail preservation and noise removal is obtained. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. Pixels that are detected as noisy are filtered, the others remain unchanged. Filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).IEEE Transactions on Image Processing 05/2011; · 3.04 Impact Factor -
Conference Proceeding: A fuzzy filter for random impulse noise removal from video
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ABSTRACT: We present a new filter for image sequences corrupted with random impulse noise. The main goal is to optimally combine noise removal with the preservation of the image details. The filtering strategy is to remove the noise in three different successive filtering steps and a fourth refinement step. In each filtering step, only the pixels that are detected as being noisy are filtered. The noise detection is achieved by fuzzy rules. To exploit the temporal information in image sequences as much as possible, detected pixels are filtered in a motion compensated way. The experimental results show clearly that the proposed method outperforms other state-of-the-art filters both numerically (in terms of the peak-signal-to-noise ratio) and visually.Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American; 08/2010 -
Article: Fuzzy adjunctions and fuzzy morphological operations based on implications
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ABSTRACT: The concept of adjunction plays an important role in mathematical morphology. If the morphological operations, dilation and erosion form an adjunction in a complete lattice, then they, as well as the closing and opening constructed by them, will fulfill certain required properties in an algebraic context. In the context of fuzzy mathematical morphology, which is an extension of binary morphology to gray-scale morphology based on fuzzy set theory, we use conjunctions and implications to define fuzzy dilations and fuzzy erosions. In this paper, we investigate when these pairs of dilations and erosions form a fuzzy adjunction, which is also defined by an implication. We find that the so-called adjointness between a conjunction and an implication plays an important role here. Finally, we develop a theorem stating that a conjunction that is adjoint with an implication cannot only be generated by an R-implication but also by other implications. This allows the easy construction of fuzzy adjunctions. © 2009 Wiley Periodicals, Inc.International Journal of Intelligent Systems 10/2009; 24(12):1280 - 1296. · 1.65 Impact Factor -
Article: Real-Time Constrained Fuzzy Arithmetic
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ABSTRACT: Klir introduced constrained fuzzy arithmetic (CFA) as a solution to the unnecessary precision loss when dealing with fuzzy quantities that represent linguistic variables. Since then, some attempts have been made to make CFA efficient, but none of these solutions is suitable for real-time applications. In this paper, we will propose a new CFA algorithm that can be used in such environments.IEEE Transactions on Fuzzy Systems 07/2009; · 4.26 Impact Factor -
Conference Proceeding: Modelling nearness and cardinal directions between fuzzy regions
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ABSTRACT: A significant part of real-world spatial information is affected by vagueness. For example, boundaries of non-administrative geographical regions tend to be ill-defined, while information about the nearness and relative orientation of two places is typically expressed through vague linguistic descriptions. In this paper, we propose a general framework to represent such information, using the concept of relatedness measures for fuzzy sets. Regions are represented as fuzzy sets in a two-dimensional Euclidean space, and nearness and relative orientation are expressed as fuzzy relations. To support fuzzy spatial reasoning, we derive transitivity rules and provide efficient techniques to deal with the complex interactions between nearness and cardinal directions.Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008 -
Chapter: An Interval-Valued Fuzzy Morphological Model Based on Lukasiewicz-Operators
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ABSTRACT: Mathematical morphology is a well-known theory to process binary, grayscale or color images. In this paper, we introduce interval-valued fuzzy mathematical morphology as an extension of classical and fuzzy morphology. It originates from the observation that the pixel values of a grayscale image are not always certain, and models this uncertainty using interval-valued fuzzy set theory. In this way, we are able to incorporate the uncertainty regarding measured pixel values into the toolbox of morphological operators. We focus our attention on a morphological model whose underlying logical framework is based on the Lukasiewicz-operators. For this model we investigate and discuss general theoretical properties, some computational aspects, as well as its relation to fuzzy morphology and classical grayscale morphology.05/2008: pages 601-612; -
Article: Fuzzifying Allen's Temporal Interval Relations
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ABSTRACT: When the time span of an event is imprecise, it can be represented by a fuzzy set, called a fuzzy time interval. In this paper, we propose a framework to represent, compute, and reason about temporal relationships between such events. Since our model is based on fuzzy orderings of time points, it is not only suitable to express precise relationships between imprecise events (ldquoRoosevelt died before the beginning of the Cold Warrdquo) but also imprecise relationships (ldquoRoosevelt died just before the beginning of the Cold Warrdquo). We show that, unlike previous models, our model is a generalization that preserves many of the properties of the 13 relations Allen introduced for crisp time intervals. Furthermore, we show how our model can be used for efficient fuzzy temporal reasoning by means of a transitivity table. Finally, we illustrate its use in the context of question answering systems.IEEE Transactions on Fuzzy Systems 05/2008; · 4.26 Impact Factor -
Chapter: The Possibilities of Fuzzy Logic in Image Processing
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ABSTRACT: It is not a surprise that image processing is a growing research field. Vision in general and images in particular have always played an important and essential role in human life. Not only as a way to communicate, but also for commercial, scientific, industrial and military applications. Many techniques have been introduced and developed to deal with all the challenges involved with image processing. In this paper, we will focus on techniques that find their origin in fuzzy set theory and fuzzy logic. We will show the possibilities of fuzzy logic in applications such as image retrieval, morphology and noise reduction by discussing some examples. Combined with other state-of-the-art techniques they deliver a useful contribution to current research.11/2007: pages 198-208; -
Article: A New Fuzzy Color Correlated Impulse Noise Reduction Method
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ABSTRACT: A new impulse noise reduction method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise reduction performance. In this paper, we discuss an alternative technique which gives a good noise reduction performance while much less artefacts are introduced. The main difference between the proposed method and other classical noise reduction methods is that the color information is taken into account to develop (1) a better impulse noise detection method and (2) a noise reduction method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed method provides a significant improvement on other existing filters.IEEE Transactions on Image Processing 11/2007; · 3.04 Impact Factor -
Article: Solving Systems of Linear Fuzzy Equations by Parametric Functions
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ABSTRACT: Buckley and Qu proposed a method to solve systems of linear fuzzy equations. Basically, in their method the solutions of all systems of linear crisp equations formed by the alpha-levels are calculated. We propose in this paper a new method for solving systems of linear fuzzy equations based on a practical algorithm using parametric functions in which the variables are given by the fuzzy coefficients of the system. We show that our algorithm is much more efficient than the method of Buckley and Qu.IEEE Transactions on Fuzzy Systems 07/2007; · 4.26 Impact Factor -
Article: A Fuzzy Noise Reduction Method for Color Images
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ABSTRACT: A new fuzzy filter is presented for the reduction of additive noise for digital color images. The filter consists of two subfilters. The first subfilter computes fuzzy distances between the color components of the central pixel and its neighborhood. These distances determine in what degree each component should be corrected. All performed corrections preserve the color component distances. The goal of the second subfilter is to correct the pixels where the color components differences are corrupted so much that they appear as outliers in comparison to their environment. Experimental results show the feasibility of the proposed approach. We compare with other noise reduction methods by numerical measures and visual observations. We also illustrate the performance of the proposed method as preprocessing step for edge detectionIEEE Transactions on Image Processing 06/2007; · 3.04 Impact Factor -
Article: Uncertainty Modeling by Bilattice-Based Squares and Triangles
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ABSTRACT: In this paper, Ginsberg's/Fitting's theory of bilattices, and in particular the associated constructs of bilattice-based squares and triangles, is introduced as an attractive framework for the representation of uncertain and potentially conflicting information, paralleling Goguen's L-fuzzy set theory. We recall some of the advantages of bilattice-based frameworks for handling fuzzy sets and systems, provide the related structures with adequately defined graded versions of the basic logical connectives, and study their properties and relationshipsIEEE Transactions on Fuzzy Systems 05/2007; · 4.26 Impact Factor -
Article: On the extension of classical propositional logic by means of a triangular norm
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ABSTRACT: In this article, we introduce a generalized extension principle by substituting a more general triangular norm T for the min intersection operator in Zadeh's extension principle. We also introduce a family of propositional logics, sup- T extension logics, obtained by the extension of classical-logical functions. A few general properties of these sup-T extension logics are derived. It is also shown that classical binary logic and the Kleene ternary logic are special cases of these logics for any choice of T, obtained by a convenient restriction of the truth domain. the very practical decomposability property of classical logic is furthermore shown to hold for the sup-min extension logic, albeit in a somewhat more limited form.International Journal of Intelligent Systems 03/2007; 5(3):307 - 322. · 1.65 Impact Factor -
Article: Fuzzy Two-Step Filter for Impulse Noise Reduction From Color Images
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ABSTRACT: A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the imageIEEE Transactions on Image Processing 12/2006; · 3.04 Impact Factor -
Conference Proceeding: A New Fuzzy Filter for the Reduction of Randomly Valued Impulse Noise
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ABSTRACT: In many current impulse noise models for images, corrupted pixels are replaced with values equal or near the maximum or minimum intensity values of the allowable dynamic range. In this paper, we present a new fuzzy filter for a more general noise model in which a noisy pixel has an arbitrary value in the dynamic range according to some underlying probability distribution. This filter consists of (1) a fuzzy detection method, where we investigate if a certain pixel position can be seen as noisy or not and (2) a fuzzy reduction method that reduces the noise while preserving the fine details (like edges and textures) of the image. Experimental results have shown that the proposed filter may be used for efficient removal of randomly valued impulse noise without distorting the useful information in the imageImage Processing, 2006 IEEE International Conference on; 11/2006 -
Conference Proceeding: Fuzzy finite elements: combination of Guyan reduction and a new method to solve linear fuzzy system
Proceedings of the International Conference on Noise and Vibration Engineering ISMA 2006; 09/2006 -
Article: A fuzzy impulse noise detection and reduction method
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ABSTRACT: Removing or reducing impulse noise is a very active research area in image processing. In this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and reduction method (FIDRM). It can also be applied to images having a mixture of impulse noise and other types of noise. The result is an image quasi without (or with very little) impulse noise so that other filters can be used afterwards. This nonlinear filtering technique contains two separated steps: an impulse noise detection step and a reduction step that preserves edge sharpness. Based on the concept of fuzzy gradient values, our detection method constructs a fuzzy set impulse noise. This fuzzy set is represented by a membership function that will be used by the filtering method, which is a fuzzy averaging of neighboring pixels. Experimental results show that FIDRM provides a significant improvement on other existing filters. FIDRM is not only very fast, but also very effective for reducing little as well as very high impulse noise.IEEE Transactions on Image Processing 06/2006; · 3.04 Impact Factor -
Conference Proceeding: Image interpolation using mathematical morphology
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ABSTRACT: We present a new method for interpolating binary images that outperforms existing techniques. Bitmapped images have a specific horizontal and vertical resolution. When we magnify such an image, we want the resolution to be increased, allowing more details in the image. However, these extra details are not present in the original image. A blowup of the image using simple interpolation will introduce jagged edges, also called "jaggies". We present a new interpolation technique "mmINT", which avoids these errors. It is based on mathematical morphology, a theoretical framework to alter an image while preserving the image objects' geometry. The algorithm detects jaggies in the blown up image and removes them, making the edges smoother. This is done by replacing specific black pixels with white pixels, and vice versa. The results show that mmINT is a superior technique for the interpolation of binary images, like logos, diagrams, cartoons and mapsDocument Image Analysis for Libraries, 2006. DIAL '06. Second International Conference on; 05/2006 -
Conference Proceeding: A survey on the use and the construction of fuzzy similarity measures in image processing
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ABSTRACT: First Page of the ArticleComputational Intelligence for Measurement Systems and Applications, 2005. CIMSA. 2005 IEEE International Conference on; 08/2005 -
Conference Proceeding: Fuzzy Filters for Noise Reduction: The Case of Gaussian Noise
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ABSTRACT: Noise reduction is a well-known problem in image processing. The reduction of noise in an image sometimes is as a goal itself, and sometimes is considered as a pre-processing step. Besides the classical filters for noise reduction, quite a lot of fuzzy inspired filters have been proposed during the past years. However, it is very difficult to judge the quality of this wide variety of filters. For which noise types are they designed? How do they perform for those noise types? How do they perform compared to each other? Can we select filters that clearly outperform the others? Is there a difference between numerical and visual results? In this paper, we answer these questions for images that are corrupted with Gaussian noiseFuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on; 06/2005
Top co-authors
Top Journals
Institutions
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1994–2011
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Ghent University
- Department of Applied Mathematics and Computer Science
Gent, VLG, Belgium
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