Autofocus using adaptive prediction approximation combined search for the fluorescence microscope in...

Area of Science:

• Biotechnology
• Microscopy
• Genomics

Background:

• High-speed image acquisition is crucial for DNA sequencing systems.
• Accurate autofocus is essential for maintaining image quality during rapid data capture.

Purpose of the Study:

• To develop and evaluate a robust and fast passive autofocus algorithm for second-generation DNA sequencing systems.
• To improve the efficiency of focus measurement and focus search processes.

Main Methods:

• Normalized variance was selected as the focus measurement (FM) algorithm.
• A novel focus search (FS) algorithm, adaptive prediction approximation combined search (APACS), was developed.
• APACS integrates golden section search (GSS) with curve-fitting search (CFS) and employs a novel initial vector estimation method.

Main Results:

• APACS demonstrated stable performance and reduced focus time compared to GSS and CFS.
• The combined centroid and least squares estimation method accelerated the search by reducing iterations.
• Simulations and experimental results validated the algorithm's effectiveness.

Conclusions:

• APACS is a robust and efficient autofocus solution for fluorescence microscopes in DNA sequencing.
• The developed algorithm significantly enhances image acquisition speed and stability.
• APACS offers a promising advancement for next-generation sequencing technologies.