Acceleration of autofocusing with improved edge extraction using structure tensor and Schatten norm.

Area of Science:

• Optics and Imaging Science
• Computational Imaging
• Image Processing

Background:

• Accurate focal plane determination is crucial for optical imaging modalities like microscopy and photography.
• Existing autofocusing algorithms face challenges in achieving rapid response times.
• Previous work introduced a structure tensor-based autofocusing algorithm for digital holography.

Purpose of the Study:

• To extend the structure tensor-based autofocusing method to broader imaging modalities beyond digital holography.
• To significantly accelerate the computation speed of the autofocusing algorithm.
• To demonstrate the algorithm's capability in edge extraction.

Main Methods:

• Implementation of an optimized computation scheme for the structure tensor.
• Utilization of the Schatten matrix norm instead of the vector norm for enhanced computational efficiency.
• Validation using synthesized and experimental data from diverse imaging scenarios.

Main Results:

• Achieved a fivefold increase in computation speed for the autofocusing algorithm.
• Maintained autofocusing accuracy comparable to previous methods.
• Successfully demonstrated the edge extraction capability by retrieving intermediate tensor images.

Conclusions:

• The enhanced structure tensor-based autofocusing algorithm offers a significant speed improvement for optical imaging.
• The method is effective across various imaging modalities, including incoherent microscopy and photography.
• The algorithm provides a robust solution for rapid and accurate focal plane determination.