Probabilistic decision based filter to remove impulse noise using patch else trimmed median

Balasubramanian, G.; Chilambuchelvan, A.; Vijayan, S.; Gowrison, G.

doi:10.1016/j.aeue.2016.01.013

Cited by 30 publications

(15 citation statements)

References 19 publications

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“…Recently, because of its robust decision for noise detection and removal, the decision‐based median filters [30–33] were proposed to remove impulse noises at high densities. Probabilistic decision‐based filter (PDBF) in [33] first proposed the patch median of neighbourhood of adaptive size for noise removal; the patch median is an improved version of median, which avoids the possibility of averaging two pixels in the middle while searching for median value.…”

Section: Related Workmentioning

confidence: 99%

“…A weighted median filter [5–7] tried to improve the standard median filter by weighting the neighbour pixels, but all the pixels in the whole image were processed uniformly without local treatment. So, further improvements were contributed in literatures [5, 8–33] in order to effectively remove impulse noises. Nonetheless, all these works even introduce new disadvantages while dealing with the drawback in the previous methods, or obtain little improvement to the previous methods, and thus are not necessarily effective.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative grouping median filter for removal of fixed value impulse noise

Chen

Zhan

Cao

et al. 2019

IET Image Processing

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative grouping median filter for removal of fixed value impulse noise

Chen

Zhan

Cao

et al. 2019

IET Image Processing

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“…This leads to the development of Un-symmetric Trimmed Median Filter (UTMF) [13] where the uncorrupted pixel elements are arranged in increasing or decreasing order in a selected window for the calculation of median after removing pixels with intensity value "0" and "255". UTMF is applied in many state-of-the-art algorithms like Decision Based Un-symmetric Trimmed Median Filter (DBUTMF) [13] [14], and Probabilistic Decision Based Filter (PDBF) [15]. DBUTMF is developed to overcome the drawback of decision based algorithm (DBA) [13] where the median value after calculation comes to be either "0" or "255".…”

Section: Introductionmentioning

confidence: 99%

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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“…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. [19] An iterative nonlocal mean filtering algorithm is proposed to remove impulse noise.…”

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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Probabilistic decision based filter to remove impulse noise using patch else trimmed median

Cited by 30 publications

References 19 publications

Iterative grouping median filter for removal of fixed value impulse noise

Iterative grouping median filter for removal of fixed value impulse noise

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

Research on Noise Reduction and Enhancement of Weld Image

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