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Adaptive image selection method for focus stacking based on a low-level vision task-driven network and liquid lens.

Jiale Wei, Shanshan Wang, Qun Hao

|August 12, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive method for capturing focal stack images, using a vision network and liquid lens to minimize images while maintaining quality. This approach enables faster, more accurate all-in-focus image reconstruction.

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Area of Science:

Computer Vision
Image Processing
Optics

Background:

All-in-focus (AIF) images are crucial in microscopy, medical imaging, and computer vision.
Focus stacking is a key technique for generating AIF images, but effective capture of focal stack images remains a challenge.

Purpose of the Study:

To propose an adaptive image selection method for efficient focal stack image acquisition.
To maintain integral image quality using a minimal number of focal stack images.
To improve the accuracy and speed of all-in-focus image reconstruction.

Main Methods:

Developed a low-level vision task-driven network, FocalAIF-Net, comprising a two-branch FocalNet and an auxiliary AIFNet.
FocalNet estimates blur and focal maps from defocused images using depth information.
Integrated a liquid lens for swift zooming guided by a decision algorithm for real-world experiments.

Main Results:

FocalAIF-Net demonstrated acceptable generalization performance across three benchmark datasets.
Real-world experiments confirmed the effectiveness of the adaptive method using a liquid lens.
The proposed method acquired focal stacks that resulted in more accurate AIF images with reduced processing time compared to traditional methods.

Conclusions:

The proposed adaptive image selection method significantly enhances the efficiency and effectiveness of focal stack image acquisition.
This approach offers a superior alternative to common average interval methods, especially when combined with liquid lens technology.
The study contributes to advancing all-in-focus imaging techniques for various applications.