The Experts below are selected from a list of 19329 Experts worldwide ranked by ideXlab platform
Qianjin Feng - One of the best experts on this subject based on the ideXlab platform.
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structure adaptive fuzzy estimation for random valued Impulse Noise suppression
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Yang Chen, Huazhong Shu, Limin Luo, Jean-louis Coatrieux, Yudong Zhang, Jian Yang, Qianjin FengAbstract:Noise detection accuracy is crucial in suppressing random-valued Impulse Noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued Impulse Noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued Impulse Noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as Noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization.
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Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Y. Chen, Yudong Zhang, Jian Yang, H. Shu, L. Luo, J.-l. Coatrieux, Qianjin FengAbstract:Noise detection accuracy is crucial in suppressing random-valued Impulse Noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued Impulse Noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued Impulse Noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as Noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization. © 2016 IEEE.
Shufang Xu - One of the best experts on this subject based on the ideXlab platform.
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a detection statistic for random valued Impulse Noise
IEEE Transactions on Image Processing, 2007Co-Authors: Yiqiu Dong, Raymond H Chan, Shufang XuAbstract:This paper proposes an image statistic for detecting random-valued Impulse Noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued Impulse Noise, even for Noise levels as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and Noise detection
Vladimir Crnojevic - One of the best experts on this subject based on the ideXlab platform.
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universal Impulse Noise filter based on genetic programming
IEEE Transactions on Image Processing, 2008Co-Authors: Nemanja Petrovic, Vladimir CrnojevicAbstract:In this paper, we present a novel method for Impulse Noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining Noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of Impulse Noise, in contrast to many existing filters which are specialized only for a particular Noise model. In addition, we propose the usage of a new Impulse Noise model-the mixed Impulse Noise, which is more realistic and harder to treat than existing Impulse Noise models. The proposed model is the combination of commonly used Noise models: salt-and-pepper and uniform Impulse Noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.
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ACIVS - Impulse Noise detection based on robust statistics and genetic programming
Advanced Concepts for Intelligent Vision Systems, 2005Co-Authors: Nemanja Petrović, Vladimir CrnojevicAbstract:A new Impulse detector design method for image Impulse Noise is presented. Robust statistics of local pixel neighborhood present features in a binary classification scheme. Classifier is developed through the evolutionary process realized by genetic programming. The proposed filter shows very good results in suppressing both fixed-valued and random-valued Impulse Noise, for any Noise probability, and on all test images.
Etienne E. Kerre - One of the best experts on this subject based on the ideXlab platform.
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a new fuzzy color correlated Impulse Noise reduction method
IEEE Transactions on Image Processing, 2007Co-Authors: Stefan Schulte, Samuel Morillas, Valentin Gregori, Etienne E. KerreAbstract: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.
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fuzzy random Impulse Noise reduction method
Fuzzy Sets and Systems, 2007Co-Authors: Stefan Schulte, Mike Nachtegael, D. Van Der Weken, V. Witte, Etienne E. KerreAbstract:A new two-step fuzzy filter that adopts a fuzzy logic approach for the enhancement of images corrupted with Impulse Noise is presented in this paper. The filtering method (entitled as Fuzzy Random Impulse Noise Reduction method (FRINR)) consists of a fuzzy detection mechanism and a fuzzy filtering method to remove (random-valued) Impulse Noise from corrupted images. Based on the criteria of peak-signal-to-Noise-ratio (PSNR) and subjective evaluations we have found experimentally, that the proposed method provides a significant improvement on other state-of-the-art methods.
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fuzzy two step filter for Impulse Noise reduction from color images
IEEE Transactions on Image Processing, 2006Co-Authors: Stefan Schulte, Mike Nachtegael, D. Van Der Weken, V. Witte, Etienne E. KerreAbstract: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
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A fuzzy Impulse Noise detection and reduction method
Image Processing IEEE Transactions on, 2006Co-Authors: Stephan Schulte, Mike Nachtegael, D. Van Der Weken, V. Witte, Etienne E. KerreAbstract: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.
Yiqiu Dong - One of the best experts on this subject based on the ideXlab platform.
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a detection statistic for random valued Impulse Noise
IEEE Transactions on Image Processing, 2007Co-Authors: Yiqiu Dong, Raymond H Chan, Shufang XuAbstract:This paper proposes an image statistic for detecting random-valued Impulse Noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued Impulse Noise, even for Noise levels as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and Noise detection
