Impulse noise removal by an adaptive trust-region method

Kimiaei, Morteza; Rahpeymaii, Farzad

doi:10.1007/s00500-018-03745-y

Cited by 4 publications

(1 citation statement)

References 70 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%

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%

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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A new family of Polak–Ribière–Polyak conjugate gradient method for impulse noise removal

Mousavi,

Esmaeilpour,

Sheikhahmadi

2023

Soft Comput

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In this paper, impulse noise removal problem is formulated as an unconstrained optimization problem with smooth objective function. It can be solved by conjugate gradient methods with desired properties (low memory and strong global convergence) in high dimensions. Accordingly, a family of the Polak-Ribière-Polyak (PRP) conjugate gradient directions is constructed for which the descent condition holds. In other words, we introduce four improved versions of PRP method three of which are based on a regularization and one of which is the combination of Fletcher-Reeves and PRP conjugate gradient parameters. Using several images, it is shown that the new methods are very robust and efficient in comparison with other competitive methods for impulse noise removal, especially in terms of the peak signal to noise ratio (PSNR).

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Two efficient three-term conjugate gradient methods for impulse noise removal from medical images

Mousavi,

Esmaeilpour,

Sheikhahmadi

2023

Multimed Tools Appl

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Impulse noise removal by an adaptive trust-region method

Cited by 4 publications

References 70 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

A new family of Polak–Ribière–Polyak conjugate gradient method for impulse noise removal

Two efficient three-term conjugate gradient methods for impulse noise removal from medical images

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