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Trimmed Mean01:10

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While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Mean Absolute Deviation01:13

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
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Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the quantitative data set is plotted in the form of a table. Or else, the data values are grouped into the quantity’s intervals, which form classes, and their respective frequencies are known. That is, the data values are distributed over different categories or classes. This is known as frequency distribution.
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Root Mean Square00:57

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If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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A new approach for SPN removal: nearest value based mean filter.

Bülent Turan1

  • 1Department of Computer Engineering/Faculty of Engineering and Architecture, Tokat Gaziosmanpasa University, Tokat, Turkey.

Peerj. Computer Science
|December 19, 2022
PubMed
Summary

A new Nearest Value Based Mean Filter (NVBMF) effectively removes salt and pepper noise (SPN) from images. This two-stage adaptive filter outperforms existing methods in image denoising tasks.

Keywords:
Image denoisingImage noise filterNoise removalSPNSPN filterSalt and pepper noiseWeighted mean filter

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Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Salt and pepper noise (SPN) is a common artifact in digital images.
  • Effective noise reduction is crucial for maintaining image quality and subsequent analysis.

Purpose of the Study:

  • To propose a novel adaptive filter, the Nearest Value Based Mean Filter (NVBMF), for eliminating SPN.
  • To evaluate the performance of NVBMF against various existing denoising techniques.

Main Methods:

  • The NVBMF employs a two-stage approach: replacing noisy pixels with the closest valid pixel value or their average, followed by updating with an average filter correlated to the noise ratio.
  • Performance was assessed using Peak Signal-to-Noise Ratio (PSNR), Image Enhancement Factor (IEF), and Structural Similarity Index Map (SSIM) metrics.
  • Comparisons were conducted on diverse image datasets under nine different noise levels.

Main Results:

  • NVBMF demonstrated superior performance, achieving the best results in 52/84 comparisons for PSNR, 47/84 for SSIM, and 36/84 for IEF.
  • The filter consistently yielded near-optimal results even when not achieving the absolute best score.
  • NVBMF proved effective across multiple image datasets and noise intensities.

Conclusions:

  • The Nearest Value Based Mean Filter (NVBMF) is a highly effective method for denoising images corrupted by salt and pepper noise.
  • NVBMF offers a robust and competitive solution for image noise reduction compared to existing adaptive filters.