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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
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ABSTRACT: In this paper a new filtering framework for colour image sequences corrupted by random impulse noise is introduced. The proposed method consists of three successive filtering steps in order to find a good trade-off between detail preservation and noise removal. One hard filtering step, that should remove all the noise at once, would namely also remove a considerable amount of details. In the different noise detection steps, we make use of human knowledge represented in the form of fuzzy if-then rules. The detection of noisy pixel components is based on spatial and temporal information as well as on information from the other colour bands. Additionally, only detected pixel components are filtered by blockmatching based on an adaptation of the mean absolute difference (MAD) to noise. The other components remain unchanged. From the experimental results, it can be concluded that the proposed filter outperforms other state-of-the-art impulse noise filters.
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on; 08/2010
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ABSTRACT: Mathematical morphology is a theory to extract specific information such as edges and patterns from images. The original binary morphology, for binary black and white images, was extended to greyscale images, amongst others, by a fuzzy approach known as fuzzy mathematical morphology. This approach was based on the observation that greyscale images and fuzzy sets can be modelled in the same way. Recently, fuzzy mathematical morphology has been further extended based on extensions of classical fuzzy set theory. In this paper, we focus on the extension based on interval-valued fuzzy set theory, i.e., interval-valued fuzzy morphology, and we give an overview of the basic properties that hold in this model.
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on; 08/2010
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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
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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
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ABSTRACT: The concept of an L-fuzzy set generalizes not only the concept of a fuzzy set but also the concepts of interval-valued fuzzy sets and intuitionistic fuzzy sets (as will become clear in this paper). In addition, the class of L-fuzzy sets forms a complete lattice whenever the underlying set L constitutes a complete lattice. Based on these observations, we develop a general approach towards L-fuzzy mathematical morphology in this paper. Our focus is in particular on the construction and on the properties of interval-valued and intutionistic fuzzy mathematical morphologies that arise as special, isomorphic cases of L-fuzzy mathematical morphology.
Fuzzy Information Processing Society, 2009. NAFIPS 2009. Annual Meeting of the North American; 07/2009
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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;
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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;
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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 image
IEEE Transactions on Image Processing 12/2006; · 3.04 Impact Factor
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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 image
Image Processing, 2006 IEEE International Conference on; 11/2006
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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
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ABSTRACT: We constructed several new fuzzy similarity measures for greyscale images that outperform the classical measures of comparison, like root mean square error or peak signal to noise ratio, in the sense of image quality evaluation. In this paper we investigate the usefulness of similarity measures for the comparison of colour images. Instead of applying the similarity measures for greyscale images component-wise, we extend the similarity measures to colour images by applying vector morphological operators. We restrict ourselves to an investigation in the RGB colour space
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on; 12/2005
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Computational Intelligence for Measurement Systems and Applications, 2005. CIMSA. 2005 IEEE International Conference on; 08/2005
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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 noise
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on; 06/2005
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ABSTRACT: In this paper we discuss an extensive comparative study of 38 different classical and fuzzy filters for noise reduction, both
for impulse noise and gaussian noise. The goal of this study is twofold: (1) we want to select the filters that have a very
good performance for a specific noise type of a specific strength; (2) we want to find out whether fuzzy filters offer an
added value, i.e. whether fuzzy filters outperform classical filters. The first aspect is relevant since large comparative
studies did not appear in the literature so far; the second aspect is relevant in the context of the use of fuzzy techniques
in image processing in general.
05/2005: pages 658-665;
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ABSTRACT: A new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions. The filter can be applied iteratively to effectively reduce heavy noise. In particular, the shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. A statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions. Experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.
IEEE Transactions on Fuzzy Systems 09/2003; · 4.26 Impact Factor
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ABSTRACT: Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures can be useful for the evaluation and comparison of different algorithms; designed to solve a particular problem. For example, one of the possible applications is the comparison of different filters for image noise reduction. It is well-known that classical quality measures, such as the MSE (mean square error) or the PSNR (peak signal to noise ratio), do not always correspond to visual observations. Therefore, several researchers are - and have been - looking for new quality measures, better adapted to human perception. The existing similarity measures are all pixel-based, and have therefore not always satisfactory results. To cope with this drawback, we propose a similarity measure based on a neighbourhood, so that the relevant structures of the images are observed very well. The new similarity measure is designed especially for use in image processing.
Signal Processing, 2002 6th International Conference on; 09/2002
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ABSTRACT: Fuzzy techniques can be applied in several domains of image processing. In this paper we show how notions of fuzzy set theory are used in establishing measures for image quality evaluation. Objective quality measures or measures of comparison are of great importance in the field of image processing. Such measures are necessary for the evaluation and the comparison of different algorithms that are designed to solve a similar problem, and consequently they serve as a basis on which algorithm is preferred to the other. In this paper, we show how similarity measures originally introduced to compare two fuzzy sets can be applied successfully in the domain of image processing.
Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASSP '02). IEEE International Conference on; 02/2002
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ABSTRACT: In this paper we give an overview of classical and fuzzy-classical
filters for image noise reduction. Together with our overview (2001) of
fuzzy filters, this paper can be seen as a preparation to our
comparative study of classical and fuzzy filters for image noise
reduction
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
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ABSTRACT: In this paper we give an overview of existing fuzzy filters for
image noise reduction. The paper is a sequel to our overview of
classical and fuzzy-classical filters (2001), and can be seen as a
preparation to our comparative study of classical and fuzzy filters for
image noise reduction. We discuss the ideas behind and the construction
of the following fuzzy filters: the "fuzzy inference ruled by
else-action" filters, the "fuzzy control based" filters, and the GOA
filter. Our goal is to give a consistent overview of these fuzzy
filters, and to clearly show the mutual differences between them
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
