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Related Concept Videos

Upsampling01:22

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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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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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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.
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A Second-Order Method for Removing Mixed Noise from Remote Sensing Images.

Ying Zhou1, Chao Ren1,2, Shengguo Zhang3

  • 1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)
|September 9, 2023
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Summary

A novel second-order method enhances remote sensing image denoising by combining a modified DnCNN with adaptive median filtering. This approach effectively removes mixed Gaussian and salt-and-pepper noise while preserving crucial image details and edges.

Keywords:
DnCNNadaptive median filteringmixed noisenearest neighbor pixel weighted medianremote sensing image

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Computer Vision

Background:

  • Remote sensing images are crucial for various applications.
  • Common noise types include Gaussian and salt-and-pepper noise.
  • Existing denoising methods struggle with mixed noise, leading to detail loss.

Purpose of the Study:

  • To propose a robust method for remote sensing image denoising.
  • To address limitations of current algorithms in handling mixed noise.
  • To improve the preservation of image edges and textures.

Main Methods:

  • A two-stage denoising approach was developed.
  • Stage 1: Modified DnCNN with dilated convolution and DropoutLayer for initial noise reduction.
  • Stage 2: Adaptive median filtering with weighted nearest neighbor pixels for refinement.

Main Results:

  • The proposed method effectively reduces mixed Gaussian and salt-and-pepper noise.
  • Subjective and objective evaluations show superior performance over traditional methods.
  • Denoised images exhibit enhanced clarity, natural details, and preserved edge/texture features.

Conclusions:

  • The second-order denoising method significantly improves remote sensing image quality.
  • It offers a more effective solution for mixed noise compared to existing techniques.
  • The method successfully balances noise removal with detail preservation.