Adaptive fuzzy inference system based directional median filter for impulse noise removal

Habib, Muhammad; Hussain, Ayyaz; Rasheed, Saqib; Ali, Mubashir

doi:10.1016/j.aeue.2016.02.005

Cited by 32 publications

(21 citation statements)

References 22 publications

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“…ASM [10] though provides better value of PSNR; it does not give good quality picture restoration because of miss detection at higher noise densities. The CBD [9] and AFIDM [3] are two methods among the existing methods which provide relatively better results till high noise densities. In these methods also, the detection is satisfactory up to 60% and miss detection occurs beyond that which results in blurring and haziness.…”

Section: Discussionmentioning

confidence: 99%

“…Simulations have been carried out in MATLAB R2013a. We evaluated the performance of the proposed method for a noise density varying from 75 to 95% RVIN and compared it with that of different filters like NAFSWM [14], ROAD [23], ROLD [21], TBLI [17], ASM [8], AFIDM [3], and CBD [9].…”

Section: Methodsmentioning

confidence: 99%

“…The CBD filter uses two conditions to differentiate between corrupted and uncorrupted pixels; first one is based on the noise intensity level and second is based on two predetermined threshold values [9]. The AFIDM filter consists of two major processes namely fuzzy inference system-based noise detection and noise filtering [3].…”

Section: Related Workmentioning

confidence: 99%

“…While some algorithms modify all the pixels (whether noisy or not), other advanced algorithms first detect the noisy pixels and then replace them with an estimated value. Some of these algorithms which have been considered for study and comparison with the proposed algorithm are noise adaptive fuzzy switching weighted median (NAFSWM) filter [14], rank order absolute difference (ROAD) filter [25], rank order logarithmic difference (ROLD) filter [23], triangle-based linear interpolation detection (TBLI) filter [19], adaptive switching median (ASM) filter [10], adaptive fuzzy inference system-based directional median (AFIDM) filter [3], and condition-based detection (CBD) filter [9].…”

Section: Related Workmentioning

confidence: 99%

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Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Singh

Maheswari

2018

J Image Video Proc.

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This study presents a novel noise detection algorithm which satisfactorily detects noisy pixels in images corrupted by random-valued impulse noise of high levels up to 80% noise density. Three levels of adaptive thresholds along with an auxiliary condition are used in this method which adequately addresses the drawbacks of existing methods, especially the miss detection of noise-free pixels as noisy pixels and vice versa. A noise signature is calculated for every pixel and compared with the first threshold to identify noise followed by the comparison of the central pixel with the second and third levels of thresholds. In addition to the standard deviation and mean, the concept of quartile has been used as another measure of dispersion. After detection, a fuzzy switching weighted median filter is applied to restore the corrupted image. The simulation results demonstrate that the proposed method is able to outperform the existing methods in both the detection and filtering of random-valued impulse noise in images.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Singh

Maheswari

2018

J Image Video Proc.

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“…Fuzzy mean filters are productive in their nature in terms of impulse noise removal [4][5][6]. Fuzzy logic used in concoction with the standard median filter have resulted in various efficient and more robust techniques as compared to fuzzy mean filters for impulse noise removal and detection [7][8][9][10][11][12]. Histogram of an image acts as an efficient tool for retrieving information.Histogram based fuzzy algorithms are also present in literature which works well for the assessment of impulse noise in images.…”

Section: Introductionmentioning

confidence: 99%

Edge Preserving Fuzzy Filter for Suppression of Impulse Noise in Images

2019

ijeat

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Impulse noise in images culminates to the loss of valuable information. This paper proposes an efficient fuzzy filter for suppression of impulse noise in images (EFFSIN). Firstly, impulse noise is effectively detected by the proposed algorithm with the utilization of Fuzzy C-Means (FCM) in two stages.Only legitimate pixels are restored after fair distiction of noisy pixel from edge pixel. Proper restoration of noisy pixel following effective detection by proposed work leads to low miss detection(MD) and false alarm (FA) rates. Simulation results depict the efficacy of the proposed filter with reference to peaksignal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

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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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Adaptive fuzzy inference system based directional median filter for impulse noise removal

Cited by 32 publications

References 22 publications

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Edge Preserving Fuzzy Filter for Suppression of Impulse Noise in Images

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

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