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Related Concept Videos

Upsampling01:22

Upsampling

262
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Downsampling01:20

Downsampling

184
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators.

Ángel Arturo Rendón-Castro1, Dante Mújica-Vargas1, Antonio Luna-Álvarez1

  • 1Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca 62490, Mexico.

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Summary

This study introduces an efficient image denoising method using redescending M-estimators and Wiener estimation. The novel filter effectively removes various noise types from grayscale and color images, improving image quality.

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additive noiseimage processingimpulsive noisemultiplicative noisenoise filteringredescending M-estimator

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Area of Science:

  • Image processing and computer vision.
  • Signal processing and noise reduction techniques.

Background:

  • Image noise is an undesirable artifact impacting image quality during acquisition, transmission, or storage.
  • Existing denoising methods may struggle with diverse noise types and densities.

Purpose of the Study:

  • To develop an efficient and robust image denoising method.
  • To suppress impulsive, additive, and multiplicative noise effectively in both grayscale and color images.

Main Methods:

  • Incorporation of redescending M-estimators within the Wiener estimation framework.
  • Utilizing local information from the Wiener filter for noise estimation.
  • Employing robust outlier rejection based on Insha and Hampel's tripartite redescending influence functions.

Main Results:

  • The proposed filter demonstrates effective suppression of impulsive, additive, and multiplicative noise.
  • Successful application to both grayscale and color images.
  • Validation through qualitative and quantitative analysis using PSNR, MAE, and SSIM metrics.

Conclusions:

  • The developed method offers an efficient approach to image denoising.
  • The integration of M-estimators enhances robustness against various noise types.
  • The filter provides significant improvements in image quality as evidenced by objective metrics.