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A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing.

Yingxu Zhang1,2, Yingzi Li2,3, Zihang Song2,3

  • 1School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Beilstein Journal of Nanotechnology
|December 31, 2019
PubMed
Summary

A new Bayesian compressed sensing method effectively removes impulse noise from atomic force microscopy (AFM) images. This approach preserves image details and is robust to varying noise levels.

Keywords:
Bayesian compressed sensingatomic force microscopy (AFM)denoisingimage processingimpulse noise

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Area of Science:

  • Microscopy
  • Image Processing
  • Signal Processing

Background:

  • Atomic Force Microscopy (AFM) generates high-resolution surface images.
  • Impulse noise can degrade the quality of AFM images, hindering analysis.
  • Existing denoising methods may struggle with preserving fine details.

Purpose of the Study:

  • To develop a novel method for impulse noise removal in AFM images.
  • To adapt Bayesian compressed sensing techniques for AFM image denoising.
  • To enhance the robustness and detail preservation of AFM image processing.

Main Methods:

  • Transformed AFM image denoising into a compressed sensing problem.
  • Identified noisy pixels using interval and self-comparison approaches.
  • Constructed measurement matrices based on noise-free pixel locations.
  • Applied Bayesian compressed sensing with row-by-row image reconstruction.

Main Results:

  • Successfully removed impulse noise from AFM images.
  • Preserved critical image details during the denoising process.
  • Demonstrated robustness to varying noise densities within a specific range.

Conclusions:

  • The proposed Bayesian compressed sensing method is effective for AFM image denoising.
  • The technique offers improved detail preservation compared to traditional methods.
  • The method's robustness makes it suitable for diverse AFM imaging conditions.