Efficiency analysis of the image impulse noise cleaning using median filters with weighted central element

Chervyakov, Nikolay I.; Lyakhov, Pavel; Orazaev, A.R.; Valueva, Maria

doi:10.1109/sibircon.2017.8109856

Cited by 6 publications

(2 citation statements)

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“…Methods based on signal smoothing [21]- [27] are proposed for noise variance estimation. This means that the elements of a series (data set) with an irregular diagram will be replaced by a set with a smoother diagram with suppressed interference.…”

Section: Q Parameter Estimation Using Digital Smoothing Filtersmentioning

confidence: 99%

Estimation of BER Bit Error Rate Using Digital Smoothing Filters

Pęksiński

Zegliñski

Mikołajczak

et al. 2021

ELEKTRON ELEKTROTECH

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In the article, the authors present a new method of numerical determination of Q and BER parameters based on which the quality of transmission in optical fibre transmission systems is determined. The proposed method is based on the use of digital smoothing filters algorithms and does not require the knowledge of the so-called “eye diagram”, which greatly simplifies the entire measurement procedure. This method can be implemented in a simple manner in real-time systems, and in particular it is crucial for ICT systems and networks with a high bit rate of more than 10 Gbps and 40 Gbps and systems with time division or wavelength division multiplexing of 100 GBE and 400 GBE.

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Section: Q Parameter Estimation Using Digital Smoothing Filtersmentioning

confidence: 99%

Estimation of BER Bit Error Rate Using Digital Smoothing Filters

Pęksiński

Zegliñski

Mikołajczak

et al. 2021

ELEKTRON ELEKTROTECH

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show abstract

“…The Gaussian noise is generally eliminated by the mean filtering algorithm [ 34 , 35 ], i.e., replacing the pixel value with the average value of its neighbors, while the impulse noise is removed with the median filtering algorithm [ 36 , 37 ], i.e., replacing the pixel value with the median of neighboring values. The mean filtering method has the advantages of simplicity, intuitiveness, and easy implementation; however, it could cause problems such as edge blurring and unreliable mean values.…”

Section: Image Acquisition System and Preprocessingmentioning

confidence: 99%

Wear and Breakage Detection of Integral Spiral End Milling Cutters Based on Machine Vision

et al. 2021

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Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.

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