Improvement to the scanning electron microscope image colorization by adaptive tuning.

Area of Science:

• Microscopy
• Image Processing
• Computational Imaging

Background:

• Scanning electron microscopy (SEM) generates grayscale images requiring effective colorization.
• Existing Canny optimization techniques for SEM image colorization have limitations in contrast enhancement.
• Accurate colorization is crucial for detailed analysis of SEM imagery.

Purpose of the Study:

• To report an improvement to the Canny optimization technique for SEM image colorization.
• To introduce an adaptive tuning process for enhanced colorization.
• To evaluate the effectiveness of the adaptive Canny optimization technique in improving image contrast.

Main Methods:

• An adaptive tuning process was developed and integrated into the Canny optimization technique.
• Color tuning is performed adaptively by comparing original and calculated luminance values.
• The improved technique was applied to scanning electron microscope grayscale images.

Main Results:

• The adaptive Canny optimization technique provides significantly better mechanical contrast.
• The method demonstrates superior performance compared to existing SEM image colorization techniques.
• Adaptive tuning effectively enhances the visibility of fine details in grayscale SEM images.

Conclusions:

• The adaptive Canny optimization technique represents a significant advancement in SEM image colorization.
• This improved method offers enhanced mechanical contrast, aiding in detailed image analysis.
• The adaptive tuning approach is a valuable addition for processing grayscale SEM images.