Region Adaptive Fuzzy Filter: An Approach for Removal of Random-Valued Impulse Noise

Roy, Amarjit; Manam, Lalit; Laskar, Rabul Hussain

doi:10.1109/tie.2018.2793225

Cited by 37 publications

(29 citation statements)

References 38 publications

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“…Based on distribution, the impulse is divided into two forms: random valued impulse noise (RVIN) and salt and pepper noise‐SPN (also called as fixed value). From both colored and gray scale images, 2‐6 the impulse noise was removed using various complex and simple denoising techniques introduced by the past researchers 37‐39 . These techniques have failed to handle RVIN but, they better handled the SPN.…”

Section: Introductionmentioning

confidence: 99%

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Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Pugalenthi¹,

Oliver²,

Anuradha³

2020

Int J Imaging Syst Tech

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Noise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.

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Section: Introductionmentioning

confidence: 99%

“…These techniques have failed to handle RVIN but, they better handled the SPN. Recently, the evolutionary methods 3,7,8 have shown better performance in image processing applications.…”

Section: Introductionmentioning

confidence: 99%

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Pugalenthi¹,

Oliver²,

Anuradha³

2020

Int J Imaging Syst Tech

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“…There are two main properties of IN, first is that certain percentage of the image pixels in the image is corrupted with noise, and the second one is that is a significant difference in the intensity value of the noisy and noise‐free pixels. Generally, there are two types of IN models, namely salt and Pepper Noise and Random Value Noise …”

Section: Introductionmentioning

confidence: 99%

“…Generally, there are two types of IN models, namely salt and Pepper Noise and Random Value Noise. 4,5 While considering the Salt and Pepper Noise, the noisy pixel have two values, say the minimum value to be I min and the maximum value to be I max which has a dynamic range of ( I min , I max ). When Random Noise is considered, the noisy pixel has a random value from the dynamic range of( I min , I max ).…”

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confidence: 99%

Efficient impulse noise removal using hybrid neuro‐fuzzy filter with optimized intelligent water drop technique

Devi¹,

Soranamageswari²

2019

Int J Imaging Syst Tech

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Impulse noise (IN) affects the digital image, during transmission, digital storage, and image acquisition. IN removal from an image is necessary as it retains the quality of the image. This work concentrates on the IN. A neuro‐fuzzy (NF) system based on a fuzzy technique which is trained by a learning algorithm derived from neural network theory was implemented for the removal of noise. A NF network for noise filtering in grayscale images that combines two NF filters with a postprocessor to produce the output was presented. However, Sugeno‐type is not intuitive technique and it also less accurate. To overcome these problems, a hybrid NF filter with optimized intelligent water drop (IWD) technique is introduced, where hybridized Sugeno–Mamdani‐based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. To improve the accuracy of the assignment of membership values to each input pixels, the optimized IWD technique is utilized, as the choice of membership function decides the efficiency of the noise removal in the images. Here, Fuzzy rules have been used to obtain the filtered output. The Hybrid method maintains the accuracy of the Sugeno model and also the interpretable capability of the Mamdani model. This method is robust against the IN and it is flexible, efficient, and accurate than existing filtering method in both noise attenuation and detail preservation and it has a great scope for better real‐time applications.

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“…Recently, detect and replace approaches, also called switching filters, have become the most popular techniques to reduce impulse noise in colour images because of their simplicity, computational efficiency and high performance [1]- [12]. The most advanced approaches in this family have evolved to a little less efficient methods that try to improve performance by adapting the local region under processing [13,14]. All these methods use a crisp threshold based decision and their performance is critically influenced by the threshold setting.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy averaging filter for impulse noise reduction in colour images with a correction step

Gregori¹,

Morillas²,

Sala³

et al. 2018

Journal of Visual Communication and Image Representation

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In this paper we propose a fuzzy detection and reduction method for impulse noise in colour images. Detection is based on the fuzzyfication of a well-known statistic called ROD. The noise degrees obtained are used to reduce impulses by employing a fuzzy averaging between the input colour vector and a robust estimate of noise-free colour vector within the input neighbourhood. Fuzzy averaging has some advantages in terms of both noise reduction and detail preservation in front of detect and replace approaches because of threshold based decisions of the latter. However, robustness of the former is lower. We solve this problem by including a correction mechanism that checks the fuzzy noise degree of the output and replaces it with a robust colour vector either when noise has not been properly reduced or when a colour artefact has been introduced. We carry out a thorough study of the method parameter setting and give a convenient and robust setting. Experimental results show that our approach is very robust in front of four different types of impulse noise.

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Region Adaptive Fuzzy Filter: An Approach for Removal of Random-Valued Impulse Noise

Cited by 37 publications

References 38 publications

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Efficient impulse noise removal using hybrid neuro‐fuzzy filter with optimized intelligent water drop technique

Fuzzy averaging filter for impulse noise reduction in colour images with a correction step

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