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Hybrid high-precision autofocus method based on deep learning and variable-step hill-climbing.

Xinzhe Ma, Xiaohua Xia, Shuhao Yuan

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Summary

This study introduces a hybrid autofocus algorithm combining deep learning with a variable-step search. The novel method significantly reduces focusing errors and processing time, improving autofocus reliability in complex imaging.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Traditional autofocus algorithms struggle with local optima and slow computations in complex conditions.
Current deep learning autofocus methods face limitations in achieving high accuracy.

Purpose of the Study:

To develop a hybrid high-precision autofocus algorithm addressing limitations of existing methods.
To enhance focusing accuracy and reduce processing time in challenging imaging scenarios.

Main Methods:

Integration of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA) into ShuffleNetV2 for improved feature extraction.
Replacement of the classification layer with a fully connected structure for direct defocus distance regression.
Guidance of a variable-step local search using predicted defocus distance for accurate localization.

Main Results:

Average focusing error reduced by 47% to 91%.
Processing time decreased by 63% to 88%.
Standard deviation of average focusing errors reduced by 26% to 81%, enhancing robustness.

Conclusions:

The proposed hybrid autofocus algorithm offers an efficient and reliable solution for complex imaging.
Significant improvements in accuracy, speed, and robustness were demonstrated compared to existing methods.