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Related Experiment Video

Updated: Jun 13, 2026

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Robust Shape-from-Focus via Physics-Inspired Distortion-Aware Focal Depth Regression.

Xin Li¹, Wei Shen¹, Jian Li²

¹Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.

Sensors (Basel, Switzerland)

|June 12, 2026

Summary

This study introduces a new method for microscopic 3D measurement using shape-from-focus (SFF) that corrects for distortions on challenging surfaces. The approach significantly improves accuracy for high dynamic range and low-texture materials.

Keywords:

Shape-from-Focus channel-wise feature attention confidence-guided smoothing high dynamic range physics-guided depth regression weak-textured surfaces

More Related Videos

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

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Last Updated: Jun 13, 2026

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Area of Science:

Microscopy and 3D Imaging
Computer Vision
Optical Metrology

Background:

Shape-from-Focus (SFF) is a valuable technique for microscopic 3D measurements.
High dynamic range (HDR) and weak-textured surfaces introduce focus curve distortions (saturation, spurious peaks, low SNR).
Existing methods struggle with these distortions due to violated unimodal assumptions, limiting post-processing corrections.

Purpose of the Study:

To develop a physics-guided, distortion-aware SFF pipeline for opaque single-surface targets.
To address limitations of traditional SFF on challenging surfaces like HDR and weak-textured materials.
To enhance the accuracy and reliability of 3D microscopic measurements.

Main Methods:

Proposed a Distortion-Aware Focal Depth Regression Network (DAFDR-Net) trained on synthetic distortions.
Implemented Channel-wise Feature Attention (CFA) to reweight distortion-sensitive features.
Utilized Soft Peak Localization and confidence-guided adaptive smoothing for improved depth estimation.

Main Results:

Reduced Root Mean Square Error (RMSE) by 36.5% on an HDR free-form surface dataset compared to MRF optimization.
Compressed the 99th-percentile absolute error from 0.181 to 0.033 on HDR surfaces.
Decreased flat-region depth standard deviation by 51.3% on weak-textured monocrystalline silicon wafers.

Conclusions:

The proposed physics-guided SFF pipeline effectively mitigates focus curve distortions.
DAFDR-Net demonstrates superior performance on challenging surfaces, improving 3D measurement accuracy.
This method offers a robust solution for microscopic 3D metrology in demanding applications.