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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Adaptive AFM image reconstruction through frequency coefficient selection based on block compressed sensing.

Yifan Hu1, Yingzi Li1, Peng Cheng1

  • 1School of Physics, Beihang University, Beijing 100191, China.

Micron (Oxford, England : 1993)
|May 25, 2025
PubMed
Summary
This summary is machine-generated.

Atomic force microscopy (AFM) imaging is accelerated using a novel adaptive frequency coefficient selection method based on block compressed sensing (BCS). This technique enhances image reconstruction quality and speed for nano-scale imaging applications.

Keywords:
AdaptiveAtomic force microscopeBlock compressed sensingFrequency coefficient selection

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Area of Science:

  • Nanotechnology
  • Microscopy
  • Signal Processing

Background:

  • Atomic force microscopy (AFM) provides essential nano-scale imaging but suffers from slow point-by-point acquisition.
  • Compressed sensing (CS-AFM) accelerates imaging by reconstructing data from incomplete measurements.
  • Block compressed sensing (BCS) further reduces time but can neglect crucial frequency information.

Purpose of the Study:

  • To develop an adaptive frequency coefficient selection method for block compressed sensing in AFM.
  • To enhance the reconstruction quality of AFM images obtained via BCS.
  • To improve the speed and fidelity of nano-scale imaging.

Main Methods:

  • Proposed an adaptive frequency coefficient selection strategy within the BCS framework.
  • Applied sparse transformation to AFM image frequency domains to identify coefficients.
  • Selected partial frequency coefficients as feature information for subblock reconstruction.
  • Utilized inverse sparse transformation to generate the final high-quality reconstructed image.

Main Results:

  • The proposed adaptive BCS method achieved superior performance compared to iterative and ordinary BCS.
  • The method demonstrated the highest Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values.
  • High-quality AFM image reconstruction was maintained across various sampling ratios.
  • Reconstruction times remained comparable to existing BCS methods.

Conclusions:

  • The adaptive frequency coefficient selection method significantly enhances AFM image reconstruction quality.
  • This approach enables fast and high-fidelity nano-scale imaging using AFM.
  • The method offers a promising solution for overcoming the speed limitations of traditional AFM.