A fuzzy filter for images corrupted by impulse noise

Russo, F.; Ramponi, Giovanni

doi:10.1109/97.503279

Cited by 234 publications

(119 citation statements)

References 6 publications

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“…In this section, first of all, the performance of proposed method is evaluated by some gray level test images as visual including Sail, Baboon, Peppers, and Lena in comparison with Median Filter (MF) [28], Iterative Median Filter (IMF) [29], Fuzzy Filter (FF) [30], and proposed method in figures of 5 to 8.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

Tavassoli¹,

Yaqubi²

2011

IJCA

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Today, Biometric systems are considered superior in technological developments, because they provide a nontransferable means of identifying people not just cards or badges. The image enhancement step is designed to reduce noise in this area. The key point about an identification method that is "nontransferable" means it cannot be given or lent to another individual so nobody can get around the system they personally have to go through the control point. The image enhancement before feature extraction system can be very efficient. In this paper a new method is proposed to raise the performance of an ear verification system, since at first, using hybrid denoising method, the noises removed from ear image and then the next step denoisy image is used for verification system. Experimental results in this study show that Gaussian noises well removed from the ear images and has acceptable affect on verification accuracy.

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Section: Resultsmentioning

confidence: 99%

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

Tavassoli¹,

Yaqubi²

2011

IJCA

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“…Sun et al, [2] provided an impulse noise image filter using fuzzy sets. The successful use of fuzzy set theory performance on many domains, together with the increasing requirement for processing digital images, have been the main intentions following the efforts concentrated on fuzzy sets [5,6]. Fuzzy set hypothesis, contrasting with some other hypothesis, can offer us with knowledge-based and robust means for image processing.…”

Section: Related Workmentioning

confidence: 99%

A Novel TwoStage Impulse Noise Removal Technique based on Neural Networks and Fuzzy Decision

Saradhadevi¹,

Sundaram²

2011

IJCA

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Image enhancement is plays a vital role in various applications. There are many techniques to remove the noise from the image and produce the clear visual of the image. Moreover, there are several filters and image smoothing techniques available in the literature. All these available techniques have certain limitations. Recently, neural networks are found to be a very efficient tool for image enhancement. A novel two-stage noise removal technique for image enhancement and noise removal is proposed in this paper. In noise removal stage, an adaptive two-level feed forward Neural Network (NN) with a Modified Levenberg-Marquardt training algorithm was used to eliminate the impulse noise. In the image enhancement stage, the fuzzy decision rules inspired by the Human Visual System (HVS) are used to categorize the image pixels into human perception sensitive class and nonsensitive class, and to enhance the quality of the image. The Hyper trapezoidal fuzzy membership function is used in the proposed technique. In order to improve the sensitive regions with higher visual quality, a Neural Network (NN) is proposed. The experiment is conducted with standard image. It is observed from the experimental result that the proposed two stage technique shows significant performance when compared to existing methods.

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“…Recently, artificial advanced soft computing methods such as neural networks (NNs) [31,32] and fuzzy systems have been used for IN detection and reduction [21][22][23][24][25][26][27][28][29][30][31][32]. In the if-then-else fuzzy reasoning (FIRE) filter [21], a fuzzy logic approach is used to enhance the images degraded by IN.…”

Section: Introductionmentioning

confidence: 99%

“…In the if-then-else fuzzy reasoning (FIRE) filter [21], a fuzzy logic approach is used to enhance the images degraded by IN. The fuzzy operator is dependent on 2-stage fuzzy judgment.…”

Section: Introductionmentioning

confidence: 99%

Removing random-valued impulse noise in images using a neural network detector

Turkmen¹

2014

Turk J Elec Eng & Comp Sci

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This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.

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A fuzzy filter for images corrupted by impulse noise

Cited by 234 publications

References 6 publications

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

A Novel TwoStage Impulse Noise Removal Technique based on Neural Networks and Fuzzy Decision

Removing random-valued impulse noise in images using a neural network detector

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