A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Lal, Madan; Kaur, Lakhwinder; Gupta, Savita

doi:10.5815/ijigsp.2015.09.08

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Noise in an image is the irregular variation of color or brightness of the image created by the sensor and circuitry of a scanner or digital camera [5][6][7][8][9][10][11][12][13]. Image noise is taken as an unwanted by-product of captured image.…”

Section: A Noisementioning

confidence: 99%

“…The effect of Gaussian Blur is a filter which de-focuses an image. The blurring is dense in the center while at the edge is feathers [12][13].…”

Section: B Gaussian Blurmentioning

confidence: 99%

“…Restoration of image or Denoising is the method of getting the original image from the corrupted image given the knowledge of the degrading factors [12][13][14][15][16][17]. It is utilized to evacuate noise from the degraded image without affecting and maintaining the edges with other details.…”

Section: Various Image Restoration Filtersmentioning

confidence: 99%

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Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models

Goel¹,

Raj²

2018

IJIGSP

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For the past recent decades, image denoising has been analyzed in many fields such as computer vision, statistical signal and image processing. It facilitates an appropriate base for the analysis of natural image models and signal separation algorithms. Moreover, it also turns into an essential part to the digital image acquiring systems to improve qualities of an image. These two directions are vital and will be examined in this work. Noise and Blurring of images are two degrading factors and when an image is corrupted with both blurring and mixed noises, de-noising and de-blurring of the image is very difficult. In this paper, Gauss-Total Variation model (G-TV model) and Gaussian Mixture-Total Variation Model (GM-TV Model) are discussed and results are presented. It is shown that blurring of the image is completely removed using G-TV model; however, image corrupted with blurring and mixed noise can be recovered with GM-TV model.

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Section: A Noisementioning

confidence: 99%

“…The effect of Gaussian Blur is a filter which de-focuses an image. The blurring is dense in the center while at the edge is feathers [12][13].…”

Section: B Gaussian Blurmentioning

confidence: 99%

See 1 more Smart Citation

Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models

Goel¹,

Raj²

2018

IJIGSP

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show abstract

“…Various noise filtering techniques [3][4][5] had been used to remove such instances in the past. Only a few filtering techniques [6] had been successfully tested at higher noise contamination. Traditional nonlinear filters [7][8][9][10][11][12][13][14][15][16][17][18][19][20] worked fairly well at low noise densities, but had failed at higher ones.…”

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 % .

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“…However, the performance of the SMF is low when the corruption level is more than 60%. This is due to the fact that the number of noise-free pixels is inadequate for a good filtering result, where the noisy pixel value may be selected as the median value for this case [3,9,10]. For a high corruption level, a better SMF filtering performance can be obtained by using bigger filter sizes.…”

Section: Introductionmentioning

confidence: 99%

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Ibrahim¹,

Abdalameer²

2019

Turk J Elec Eng & Comp Sci

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Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degrades the quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique is required to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switching median filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. The implementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where the noise pixel candidates are detected based on the intensity value. Then estimation of the local noise density is done by the second processing block. Next, the third processing block filters the corrupted pixel candidates with filters of predefined size, depending on the local noise density. After that, the noise mask is updated in the fourth processing block. Finally, the fifth processing block processes the noise residuals from the third processing block by using a size adaptive filter.Experimental results from twenty standard gray-scale images of various sizes have shown that IQASMF has the ability to restore images for up to 99% of the impulse noise corruption. As compared with the other five median filter-based methods, from the measures of mean squared error (MSE) and structural similarity index (SSIM), it is shown that the performance of IQASMF is equivalent to the performance of other methods at low and medium levels of corruption.However, at high corruption levels, IQASMF has demonstrated the best performance in terms of MSE and SSIM. The outputs from IQASMF also have the best visual appearance.

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A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Cited by 10 publications

References 22 publications

Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models

Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models

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

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

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