Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window

Lu, Ching-Ta; Chen, Yung-Yue; Wang, Lingling; Chang, Chih-Hung

doi:10.1016/j.patrec.2016.06.026

Cited by 59 publications

(28 citation statements)

References 20 publications

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“…In order to demonstrate the effectiveness of the proposed filter, the proposed filter is firstly compared with five representative denoising methods include the switching median (SM) filter [ 20 ], the directional weighted median (DWM) filter [ 26 ], the modified directional weighted median (MDWM) filter [ 27 ], the modified directional weighted (MDW) filter [ 28 ] and the three-values-weighted (TVW) filter [ 30 ]. Three typical gray level images with different image features include Lena, Boat and Zelda are selected as the test images.…”

Section: Resultsmentioning

confidence: 99%

“…The adaptive iterative fuzzy (AIF) filter [ 29 ] is developed to restore the high density noise corrupted image which firstly detects the noise corrupted pixels with an adaptive fuzzy detector and then restores the noise corrupted pixels by a weighted mean filter. The three-values-weighted (TVW) approach [ 30 ] is proposed to restore the corrupted image, which firstly employs a variable-size local window to analyze each pixel with extreme values and classifies the non-extreme pixel as the maximum, the middle, or the minimum groups in the local window, then employs the ratios of these three groups to weight the non-extreme pixels, finally replaces the gray level of central pixel by the weighted gray level. The adaptive Type-2 fuzzy (ATF) approach [ 31 ] is proposed to remove salt-and-pepper noise which identifies the pixels based on their primary membership function values and then restores the noise corrupted pixels based on the detection results.…”

Section: Introductionmentioning

confidence: 99%

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A two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter

Nie

2018

PLoS ONE

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In this paper, a two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter is proposed. The first stage firstly detects the noise corrupted pixels by combining characteristic difference parameter and gray level extreme, then develops an improved adaptive median filter to firstly restore them. The second stage introduces a restoration scheme to further restore the noise corrupted pixels, which firstly divides them into two types and then applies different restoration skills for the pixels based on the classification result. One type of pixels is restored by the mean filter and the other type of pixels is restored by the proposed adaptive directional mean filter. The new filter firstly adaptively selects the optimal filtering window and direction template, then replaces the gray level of noise corrupted pixel by the mean value of pixels on the optimal template. Experimental results show that the proposed filter outperforms many existing main filters in terms of noise suppression and detail preservation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter

Nie

2018

PLoS ONE

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“…For high frequency IMFs a smaller window sized median filter is applied whereas the window size is increased for the lower frequency IMFs. These variable window median filters allow eliminating noise in high frequency components while keeping the information of lower frequency components intact [20,21].…”

Section: Memd With Median Filtermentioning

confidence: 99%

An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

Karatoprak

Şeker

2019

Mathematical Problems in Engineering

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This paper proposes an improved Empirical Mode Decomposition (EMD) method by using variable window size median filters during the Intrinsic Mode Functions (IMFs) generation. Compared to the traditional EMD, the improved EMD, namely, Median EMD (MEMD), helps to reduce mode-mixing providing an improvement in terms of separating the fundamental frequencies per IMF. The MEMD method applies the EMD to the signal and then applies a variable window size median filter to the resulting IMFs. A narrow window is used for high frequency components where a broader window is used for the lower frequency components. The filtered IMFs are then summed again and another round of EMD is applied to yield the improved MEMD IMFs. A test setup for accelerated aging of bearings in induction motors is used for the comparison of the traditional and the improved EMD methods with the goal of finding potential bearing defects in an induction motor. The potential defect at the early stage is compared with the faulty state and is used to extract the characteristics of the bearing damage that develops gradually. Comparing the EMD and MEMD, it is seen that MEMD is an improvement to EMD in terms of mode-mixing problem. The MEMD method demonstrated to have better performance compared to the traditional EMD for the extraction of the fault features from the healthy operational state of the motor.

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“…For k from N-4 to N-2: At the top row (i.e., j 1 =y-2), find noise-free pixel (α 1 (j 1 ,k)=0) and remove them from h 3 , i.e., h 3 (D(j 1 ,k))←h 3 (D(j 1 ,k))-1 At the bottom row (i.e., j 2 =y+1), find noise-free pixel (α 1 (j 2 ,k)=0) and add them to h 3 , i.e., h 3 (D(j 2 ,k))←h 3 (D(j 2 ,k))+1…”

Section: Endmentioning

confidence: 99%

“…For j from x-1 to x+1: At the right column (i.e., k 1 =x+2), find noise-free pixel (α 1 (j,k 1 )=0) and remove them from h 3 , i.e., h 3 (D(j,k 1 ))←h 3 (D(j,k 1 ))-1 At the left column (i.e., k 2 =x-1), find noise-free pixel (α 1 (j,k 2 )=0) and add them to h 3 , i.e., h 3 (D(j,k 2 ))←h 3 Unlike QASMF [23], in this third processing block, IQASMF does not process noise pixel candidates (i.e. pixels with α 1 (y, x) = 1 and β(y, x) < 9 ) that are located near the image's border.…”

Section: Endmentioning

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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Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window

Cited by 59 publications

References 20 publications

A two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter

A two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter

An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

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

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