Color differences based fuzzy filter for extremely corrupted color images

Masood, Sohail; Hussain, Ayyaz; Jaffar, M. Arfan; Choi, Tae‐Sun

doi:10.1016/j.asoc.2014.03.006

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…During the study of performance analysis, the proposed filter has been compared with state of art filters likely vector median filter (VMF) [8], centre weight vector median filter (CWVMF) [10], boundary discriminative noise detection (BDND) filter [24] and modified histogram fuzzy color (MHFC) filter [22]. Table 1 lists the performance of the various filters in removal of impulse noise from Lena image corrupted by fixed impulse noise.…”

Section: A Objective Analysismentioning

confidence: 99%

“…The boundary discriminative noise detection (BDND) [24] introduces a new noise removal technique which is effective for removal of high density impulse noise. There are some fuzzy filters [16,21,22,23], which are also effective for the removal of impulse noise. Li et al [27] has proposed s neuro-fuzzy network based impulse noise filtering for gray scale images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images

Roy

Laskar

2017

AEU - International Journal of Electronics and Communications

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Section: A Objective Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images

Roy

Laskar

2017

AEU - International Journal of Electronics and Communications

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“…However, the histogram estimation is performed during fuzzification process to consider the correlation between channels. Afterwards, the color scale images were improved by removing all the random and fixed valued impulse noise using the modified HFC (MHFC) approach of Masood et al 22 Further, the noisy pixels are classified through mapping pixel gradients to fuzzy MFs utilizing both MHFC and HFC filters of fuzzy two step filter color filter (FTSCF) 23 . However, the noisy pixels deviation has provided inappropriate weightage after using the fuzzy mapping function.…”

Section: Review Of Related Workmentioning

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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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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“…Meanwhile, fuzzy filters [21][22][23][24] got a platform as it was better at removing impulse noises from images. Such filters used fuzzy membership functions to find the fuzzy relationship between the pixels in the images and showed tremendous progress in impulse noise filtering [25][26][27] problems.…”

Section: Introductionmentioning

confidence: 99%

Impulse noise removal by k-means clustering identified fuzzy filter: a new approach

Bandyopadhyay¹,

Deb²,

Das³

et al. 2020

Turk J Elec Eng & Comp Sci

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Removal of impulse noise from corrupted digital images has been a hitch in the field of image processing. Random nature of impulse noise makes the task of noise removal more critical. Different filters have been designed for noise removal purpose and have shown formidable results mostly for low and medium level noise densities. In this paper, a new two-stage technique called k-means clustering identified fuzzy filter (KMCIFF) is proposed for de-noising gray-scale images. KMCIFF consists of a k-Means clustering-based high density impulse noise detection, followed by a fuzzy logic-oriented noise removal mechanism. In the detection process, a 5 × 5 window centering upon each pixel of the image is considered. K-Means clustering is applied on each 5 × 5 window to group the pixels into different clusters to detect whether the central pixel of each window is noisy or not. In the noise removal process, a 7 × 7 window centering upon each noisy pixel of the image, as detected by the clustering is considered. Fuzzy logic is used to find the nonnoisy pixel in each 7 × 7 window having the highest influence on the central noisy pixel of the window. Finally, that pixel is replaced by the approximated pixel intensity value calculated from the highest influencing non-noisy pixel. KMCIFF is evaluated upon seven different standard test images using peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), Percentage of actual nonnoisy pixels detected as erroneous out of the total number of pixels (PDAE) and average run time (ART). It has been observed that KMCIFF shows significantly more competitive visual and quantitative performances visa -vis most of the extant traditional filters at high noise densities of up to 90 % .

show abstract

Color differences based fuzzy filter for extremely corrupted color images

Cited by 23 publications

References 28 publications

Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images

Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images

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

Impulse noise removal by k-means clustering identified fuzzy filter: a new approach

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