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

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.