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  3. Efficient Two-stage Autofocus For Micro-assembly Based On Joint Spatial-frequency Image Quality Assessment.
  1. Home
  3. Efficient Two-stage Autofocus For Micro-assembly Based On Joint Spatial-frequency Image Quality Assessment.

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Efficient Two-Stage Autofocus for Micro-Assembly Based on Joint Spatial-Frequency Image Quality Assessment.

Jianpeng Zhang1,2, Tianbo Kang1,2, Xin Zhao1,2

  • 1National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China.

Journal of Imaging
|March 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an advanced autofocus system for micro-assembly, enhancing precision with a novel WaveMamba-IQA model. The two-stage method achieves high success rates, improving automation in delicate tasks.

Keywords:
autofocusdual-camera systemmicro-assemblymicro-visionno-reference image quality assessmentspatial-frequency modeling

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Optical Engineering

Background:

  • Micro-assembly demands precise positioning, often hindered by complex visual challenges like reflections and shallow depth-of-field.
  • Traditional autofocus methods struggle with robustness in micro-vision systems due to these challenging imaging conditions.
  • Existing sharpness metrics are often degraded by scale variations and complex reflections inherent in micro-scale environments.

Purpose of the Study:

  • To develop an efficient and robust two-stage autofocus method for dual-camera micro-vision systems.
  • To introduce WaveMamba-IQA, a novel spatial-frequency image quality assessment model for accurate sharpness estimation.
  • To enhance the automation and precision of micro-assembly systems through improved autofocus capabilities.

Main Methods:

  • Designed WaveMamba-IQA, integrating Discrete Wavelet Transform and Vision Transformers for high-frequency and semantic feature extraction.
  • Incorporated Multi-Linear Transposed Attention and Vision Mamba for advanced global context modeling in image sharpness estimation.
  • Implemented a coarse-to-fine autofocus workflow using Covariance Matrix Adaptation Evolution Strategy and geometric prior-based adjustment for dual cameras.

Main Results:

  • WaveMamba-IQA demonstrated high accuracy in sharpness estimation, achieving a Spearman correlation coefficient of 0.9786 on a microsphere dataset.
  • The integrated two-stage autofocus system achieved a 98.33% success rate across diverse lighting conditions.
  • Significant improvements in autofocus robustness and automation levels were observed compared to traditional methods.

Conclusions:

  • The proposed WaveMamba-IQA and two-stage autofocus method effectively address challenges in micro-assembly vision systems.
  • This approach offers a robust and automated solution, overcoming limitations of manual and conventional focusing techniques.
  • The enhanced precision and reliability are crucial for advancing micro-assembly system capabilities.