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Phase-ReNet: a phase regression network for feature detection from a defocused pattern for camera calibration.

Wenhui Hou, Chuanqi Zhou, Haojie Zhu

|September 22, 2025

Summary

This summary is machine-generated.

This study introduces a novel Phase-ReNet for camera calibration using crossed fringe patterns. It achieves high precision even with defocused images, improving efficiency and accuracy.

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Area of Science:

Computer Vision
Machine Learning
Optics

Background:

Accurate camera calibration is crucial for many computer vision applications.
Defocused images degrade feature detection accuracy and hinder parameter estimation.
Traditional methods often require multiple patterns and clear images.

Purpose of the Study:

To develop a robust camera calibration method for defocused images.
To improve feature detection accuracy and efficiency in challenging imaging conditions.
To enable precise camera parameter estimation from single, potentially blurred, patterns.

Main Methods:

Utilized a crossed fringe pattern as the calibration target.
Developed a phase regression network (Phase-ReNet) for wrapped phase calibration.
Recovered horizontal and vertical phase maps from a single pattern.

Main Results:

Achieved high-precision feature point extraction from defocused images.
Demonstrated feature detection accuracy comparable to traditional phase-shifting methods.
Obtained a mean reprojection error of only 0.0552 pixels for defocused cameras.

Conclusions:

The proposed Phase-ReNet method is effective for defocused camera calibration.
The single-pattern approach significantly enhances calibration efficiency.
This method offers a viable solution for applications with unavoidable image defocus.