Median Filtering: A New Insight

Villar, Sebastián A.; Torcida, Sebastián; Acosta, Gerardo

doi:10.1007/s10851-016-0694-0

Cited by 44 publications

(21 citation statements)

References 21 publications

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“…Each time the window's center shifts to its right, the kernel histogram to update requires 2 + 1 additions to and 2 + 1 removals [31]. When the window's center shifts one pixel to the right, e.g., from 4,3 to 4,4 , two updates are performed: (a) the rightmost column histogram ℎ 6 is updated by removing and adding an top 1,6 and an bottom 6,6 pixel value, respectively; (b) the kernel histogram is updated removing the leftmost column histogram ℎ 1 and adding the rightmost column histogram ℎ 6 .…”

Section: Figmentioning

confidence: 99%

“…. The percentage of shared information between successive windows thus increases really fast with the radius: a 33% of shared values for a radius = 1; a 60% of shared values for a radius = 2 , a 90% of shared values for a radius = 10 and so on [31].…”

Section: Breakdown Point Conceptmentioning

confidence: 99%

“…The Perreault and Herbert method [30] represents a variation of Huang et al algorithm using distributive histograms reaching in the worst case the lowest algorithmic complexity of

O ()(, b)

per pixel (where

b = 2^{image bit ‐ depth}

, the number of scale levels). Finally, the Villar et al method [31] demonstrated new improvements and capabilities of MF using the optimal breakdown point concept maintaining the same algorithmic complexity

O ()(, r)

and

O ()(, b)

for Huang and Perrault and Herbert versions, respectively, but reducing objective metrics computational (dynamic memory accesses, arithmetic operations, logic comparison and transition effort) that clearly outperform the corresponding standard versions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient approach for OS‐CFAR 2D technique using distributive histograms and breakdown point optimal concept applied to acoustic images

Villar

Menna

Torcida³

et al. 2019

IET Radar, Sonar & Navigation

Self Cite

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In this work, a new approach to improve the algorithmic efficiency of the Order Statistic-Constant False Alarm Rate (OS-CFAR) applied in two dimensions (2D) is presented. OS-CFAR is widely used in radar technology for detecting moving objects as well as in sonar technology for the relevant areas of segmentation and multi-target detection on the seafloor. OS-CFAR rank orders the samples obtained from a sliding window around a test cell to select a representative sample that is used to calculate an adaptive detection threshold maintaining a false alarm probability. Then, the test cell is evaluated to determine the presence or absence of a target based on the calculated threshold. The rank orders allows that OS-CFAR technique to be more robust in multi-target situations and less sensitive than other methods to the presence of the speckle noise, but requires higher computational effort. This is the bottleneck of the technique. Consequently, the contribution of this work is to improve the OS-CFAR 2D with the distributive histograms and the optimal breakdown point optimal concept, mainly from the standpoint of efficient computation. In this way, the OS-CFAR 2D on-line computation was improved, by means of speeding up the samples sorting problem through the improvement in the calculus of the statistics order. The theoretical algorithm analysis is presented to demonstrate the improvement of this approach. Also, this novel efficient OS-CFAR 2D was contrasted experimentally on acoustic images.

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Section: Figmentioning

confidence: 99%

Section: Breakdown Point Conceptmentioning

confidence: 99%

“…The Perreault and Herbert method [30] represents a variation of Huang et al algorithm using distributive histograms reaching in the worst case the lowest algorithmic complexity of

O ()(, b)

per pixel (where

b = 2^{image bit ‐ depth}

O ()(, r)

and

O ()(, b)

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient approach for OS‐CFAR 2D technique using distributive histograms and breakdown point optimal concept applied to acoustic images

Villar

Menna

Torcida³

et al. 2019

IET Radar, Sonar & Navigation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Median ltering is one of the most commonly used spatial ltering methods. It uses the median value in the kernel to substitute the value of center of the kernel [37]. So, it is easy to implement and e cient for impulsive noise and speckle noise.…”

Section: Phase Recovering Approachmentioning

confidence: 99%

Real‐Time and Quantitative Measurement of Crack‐Tip Stress Intensity Factors Using Digital Holographic Interferometry

Xia

Guo

Feng

et al. 2018

Advances in Materials Science and Engineering

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Detection of the crack in an object is a critical problem for the health monitoring of a transparent object. e real-time and quantitative measurement of the crack-tip stress intensity factor (SIF) remains an open issue. In this paper, an approach for realtime and quantitative measurement for the SIFs of a Mode I crack is presented based on digital holographic interferometry (DHI). A transmission digital holographic system is established to measure the phase difference of an object wave during loading. e expression to achieve the SIF from the phase difference is formulated. To enhance the accuracy of measurement, calibrated phase unwrapping based on least-squares and iteration and median filtering is applied to retrieve the actual phase from the noisy wrapped one. e SIFs of the Mode I crack in a transparent polymethyl methacrylate (PMMA) specimen are measured by this approach. e results are compared with the theoretical ones to demonstrate the feasibility of the proposed approach.

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“…A standard median filter [4] that removes noises with the median of neighbour pixels is still unable to well protecting the edges and details. 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.…”

Section: Introductionmentioning

confidence: 99%