An adaptive dynamically weighted median filter for impulse noise removal

Khan, Sajid Ullah; Lee, Dong-Ho

doi:10.1186/s13634-017-0502-z

Cited by 39 publications

(29 citation statements)

References 33 publications

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“…Trimmed Filter In order to design the proposed PDBATF, proposed ATF and proposed PEATF is implemented considering the facts arrived from and above sections and from the referred literature [6][7][8][9] [11][12][13] are as follows:…”

Section: Proposed Probabilistic Decision Based Averagementioning

confidence: 99%

“…The uncorrupted pixel is likewise changed by the SMF. Many variations and improved strategies based on SMF is reported till date such as Adaptive Median Filter (AMF) [6] in which the size of the filtering window is adaptive in nature, and it depends on the number of noise-free pixels in the current filtering window, Adaptive Weighted Median Filter (AWMF) [7][8] where the weight of a pixel is decided on the basis of standard deviation in four-pixel directions (vertical, horizontal and two diagonals). The detection of the noisy pixel in an image contaminated by random-valued impulse noise, is increasingly troublesome in comparison with fixed valued impulse noise, as the gray value of a noisy pixel may not be substantially larger or smaller than those of its neighbors.…”

Section: Introductionmentioning

confidence: 99%

“…In TMF, if the concerned processing pixel is noisy then the pixel elements with intensity value "0" and "255" is removed before applying the median approach. Several algorithms [6][7][8][9][10][11][12] are developed and reported with the application of TMF. In Alpha Trimmed Median Filter (ATMF) [12], trimming is symmetric which leads to blurring and loss of image details.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

Sen*,

Rout

2020

IJRTE

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The paper focuses on the evacuation of salt and pepper noise from a contaminated image. A probabilistic decision based average trimmed filter (PDBATF) is proposed for both high and low noise density. The proposed algorithm addresses the issue related to even number of noise-free pixel in trimmed median filter for the calculation of processing pixel. The proposed average trimmed filter is incorporated for low noise density while the proposed patch else average trimmed filter is applied for high noise density. Finally, they are combined together to develop the proposed PDBATF. The proposed algorithm show an excellent noise removal capability compared to the recently developed algorithms in terms of peak signal to noise ratio, image enhancement factor, mean absolute error and execution time. It works very efficiently in de-noising contaminated medical images such as chest-x-ray and malaria-blood-smear.

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Section: Proposed Probabilistic Decision Based Averagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

Sen*,

Rout

2020

IJRTE

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“…However, the standard MF alters each pixel by a median value of pixels in the predefined window, which destroys many noise-free pixels, and cannot effectively retain the high-frequency components. To improve the denoising performance and efficiency, extended versions of MF have been proposed [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The most commonly used modified MFs are the weighted filter [5,8,18] and the adaptive median filter [14,22].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Chen

Huang

et al. 2020

Symmetry

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Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.

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“…A low-illuminance nonlinear laser image edge adaptive enhancement device based on radial hill was adopted [15]. Moreover, an nsmission estimation method based on the improved dual region filter and the guide filter was proposed to reduce the complexity of image defogging algorithms and solve the white halo of image edges, and was verified experimentally to be effective and efficient [16].In reference [17], an adaptive dynamic weighted median filtering algorithm is proposed, which achieves good filtering effect. In reference [18], a block median filtering algorithm based on probability decision-making is proposed, which improves the traditional median filtering, and the effect is remarkable.…”

Section: Introductionmentioning

confidence: 99%

Research on Noise Reduction and Enhancement of Weld Image

Zhang¹,

Gao²,

Zhu³

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.

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An adaptive dynamically weighted median filter for impulse noise removal

Cited by 39 publications

References 33 publications

Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Research on Noise Reduction and Enhancement of Weld Image

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