Removing of high density salt and pepper noise using fuzzy median filter

Sravani, B.; Rao, M. V. Nageswara

doi:10.1109/ichpca.2014.7045370

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The AMF works on the detection of the corrupted pixel compared to its neighborhood in the treated window to be labeled as a noisy pixel; the size of this window may be varied according to the comparison criteria. Then, this labeled pixel is replaced by the median pixel of the tested neighbourhood [19]. AMF gives a much better result in removing the salt and pepper noise, compared with other median filter types, whether in the visual quality or Noise ratio criteria [16].…”

Section: Adaptive Filteringmentioning

confidence: 99%

Progression approach for image denoising

et al. 2019

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Removing noise from the image by retaining the details and features of this treated image remains a standing challenge for the researchers in this field. Therefore, this study is carried out to propose and implement a new denoising technique for removing impulse noise from the digital image, using a new way. This technique permits the narrowing of the gap between the original and the restored images, visually and quantitatively by adopting the mathematical concept ''arithmetic progression''. Through this paper, this concept is integrated into the image denoising, due to its ability in modelling the variation of pixels' intensity in the image. The principle of the proposed denoising technique relies on the precision, where it keeps the uncorrupted pixels by using effective noise detection and converts the corrupted pixels by replacing them with other closest pixels from the original image at lower cost and with more simplicity.

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Section: Adaptive Filteringmentioning

confidence: 99%

Progression approach for image denoising

et al. 2019

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“…Noise is simply an unnecessary detail, impacting the consistency of the signals and data [10,11]. This is because noise results in alterations in images where the original values of some pixels are changed to different values [5].…”

Section: Introductionmentioning

confidence: 99%

“…One of the common types of noise is impulse noise [12]. Impulse noise frequently distorts the image during the phase of image processing and transmission [11,13]. Impulse noise can be divided into two categories, which are random value impulse noise and fixed value impulse noise [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Impulse noise significantly impacts the processing and analysis of digital images, such as image segmentation, object recognition, object tracking and edge detection [22,23]. Thus, it is important to eliminate the impulse noise from the image before proceeding with any subsequent processing [11,23,24]. Effective impulse noise removal is therefore highly needed.…”

Section: Introductionmentioning

confidence: 99%

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Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

Mursal¹,

Ibrahim²

2020

Electronics

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It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.

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