Random Value Impulse Noise Removal Based on Most Similar Neighbors

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

doi:10.1109/fit.2015.64

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…To evaluate the performance of proposed image denoising algorithm, various state-of-the-art denoising methods such as ACWM (Caiquan & Dehua, 2008), MPSM (Kuykin et al, 2009), FIDRM (Schulte et al, 2006), DWM (Dong & Xu, 2007), FRINRM (Schulte et al, 2007), IAINS (Sa & Majhi, 2010), LNI-MTR (Hussain Dawood et al, 2017), ANN (Turkmen, 2016), MSN (Habib, Rasheed, et al, 2015), ADTMF (Gupta et al, 2015), CAFSM (Toh & Isa, 2010), AFIDM (Habib et al, 2016), NLM (Xiong & Yin, 2012), ATFDF , FPDM (Turkmen, 2013), MDW (Li et al, 2014), NWM(L. Liu, Chen, Zhou, & You, 2015), DROAD (Hussain & Habib, 2017) and TOID (Azhar et al, 2018) are used. Some of the methods are used to evaluate the noise identifier's performance while others are used for the noise filtering's performance.…”

Section: Resultsmentioning

confidence: 99%

“…' M and ' ' N represents the total number of rows and columns of an image, respectively. The other parameter used to assess the denoising performance is SSIM that is a measure to compute the degree of similarity between original noise-free and restored image and is expressed as: The proposed denoising algorithm is compared with state-of-the-art denoising methods such as MSN (Habib, Rasheed, et al, 2015), ADTMF (Gupta et al, 2015), CAFSM (Toh & Isa, 2010), AFIDM (Habib et al, 2016), NLM (Xiong & Yin, 2012), ATFDF , FPDM (Turkmen, 2013), MDW (Li et al, 2014), NWM(L. Liu et al, 2015) LNI-MTR (Hussain Dawood et al, 2017), DROAD (Hussain & Habib, 2017) and TOID (Azhar, Dawood, & Dawood, 2018); to evaluate its restoration capability, both quantitatively and visually. The quantitative comparison of proposed denoising method with stateof-the-art methods is given in Tables (1-4 indicates the improved restoration and denoising capability of the proposed method as compared to state-of-the-art methods.…”

Section: Performance Evaluation Of Overall Denoising Methodsmentioning

confidence: 99%

“…This filter acts like an average filter and hence not suitable for impulse noise removal. A filter for the restoration of RVIN corrupted pixels was proposed that considers the most similar neighboring pixels for the identification of corrupted pixels (Habib, Rasheed, Hussain, & Ali, 2015). Fuzzy rule based median filter was used at the noise removal stage.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Detail-preserving switching algorithm for the removal of random-valued impulse noise

Azhar

Dawood

et al. 2018

J Ambient Intell Human Comput

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This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tridirectional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Evaluation Of Overall Denoising Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Detail-preserving switching algorithm for the removal of random-valued impulse noise

Azhar

Dawood

et al. 2018

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

RETRACTED ARTICLE: Cartesian vector-based directional nonparametric fuzzy filter for random-valued impulse noise removal

Hussain

Habib

Ramzan

2017

SIViP

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A Comparative Review for Color Image Denoising

Ashpreet

2023

Advances in Multimedia and Interactive Technologies

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With the explosion in the number of color digital images taken every day, the demand for more accurate and visually pleasing images is increasing. Images that have only one component in each pixel are called scalar images. Correspondingly, when each pixel consists of three separate components from three different signal channels, these are called color images. Image denoising, which aims to reconstruct a high-quality image from its degraded observation, is a classical yet still very active topic in the area of low-level computer vision. Impulse noise is one of the most severe noises which usually affect the images during signal acquisition stage or due to the bit error in the transmission. The use of color images is increasing in many color image processing applications. Restoration of images corrupted by noises is a very common problem in color image processing. Therefore, work is required to reduce noise without losing the color image features.

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Random Value Impulse Noise Removal Based on Most Similar Neighbors

Cited by 4 publications

References 17 publications

Detail-preserving switching algorithm for the removal of random-valued impulse noise

Detail-preserving switching algorithm for the removal of random-valued impulse noise

RETRACTED ARTICLE: Cartesian vector-based directional nonparametric fuzzy filter for random-valued impulse noise removal

A Comparative Review for Color Image Denoising

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