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Related Concept Videos

Downsampling01:20

Downsampling

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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¹³C NMR: ¹H–¹³C Decoupling01:04

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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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Noise reduction in digital speckle pattern interferometry using bidimensional empirical mode decomposition.

María Belén Bernini1, Alejandro Federico, Guillermo H Kaufmann

  • 1Instituto de Física de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas and Universidad Nacional de Rosario, Rosario, Argentina. mbernini@ifir.edu.ar

Applied Optics
|May 13, 2008
PubMed
Summary
This summary is machine-generated.

Bidimensional empirical mode decomposition (BEMD) effectively reduces speckle noise in digital speckle pattern interferometry (DSPI) fringes. This data-driven method separates noise from fringe data without needing fixed functions or operator input.

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Area of Science:

  • Optical Metrology
  • Signal Processing

Background:

  • Speckle noise significantly degrades fringe quality in Digital Speckle Pattern Interferometry (DSPI).
  • Existing noise reduction methods may require fixed basis functions or operator intervention.

Purpose of the Study:

  • To introduce a novel Bidimensional Empirical Mode Decomposition (BEMD) method for speckle noise reduction in DSPI fringes.
  • To evaluate the performance of BEMD against a one-dimensional Empirical Mode Decomposition (EMD) approach.

Main Methods:

  • The proposed BEMD method employs a sifting process to decompose DSPI fringes into intrinsic modes representing high and low frequencies.
  • High-frequency modes are identified as speckle noise, allowing separation from lower-frequency fringe information.
  • The technique is fully data-driven, eliminating the need for predefined basis functions or manual adjustments.

Main Results:

  • BEMD successfully discriminates and removes speckle noise from DSPI fringes.
  • Simulated data analysis demonstrates the effectiveness of BEMD in denoising.
  • Comparison with 1D EMD shows comparable or improved denoising performance.

Conclusions:

  • BEMD offers a robust and automated solution for speckle noise reduction in DSPI.
  • The method is applicable to both simulated and experimental DSPI fringe data.
  • BEMD presents a valuable tool for enhancing the accuracy of DSPI measurements.