# Fringe pattern analysis using deep learning

arXiv: https://arxiv.org/abs/1807.02757 · HTML: https://mlsanity.com/paper/1807.02757/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2018.

## Built on by (most cited first, 13 of 13)

- [Physical-World Optical Adversarial Attacks on 3D Face Recognition](https://mlsanity.com/paper/2205.13412/) (2022)
- [Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning](https://mlsanity.com/paper/2406.13846/) (2024)
- [Single-shot fringe projection profilometry based on Deep Learning and Computer Graphics](https://mlsanity.com/paper/2101.00814/) (2021)
- [Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection](https://mlsanity.com/paper/2112.06759/) (2021)
- [3D Imaging of Complex Specular Surfaces by Fusing Polarimetric and Deflectometric Information](https://mlsanity.com/paper/2406.01994/) (2024)
- [AI-driven projection tomography with multicore fibre-optic cell rotation](https://mlsanity.com/paper/2312.07631/) (2023)
- [UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern](https://mlsanity.com/paper/2304.14503/) (2023)
- [Enhanced fringe-to-phase framework using deep learning](https://mlsanity.com/paper/2402.00977/) (2024)
- [Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction](https://mlsanity.com/paper/2404.06657/) (2024)
- [Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry](https://mlsanity.com/paper/2407.14198/) (2024)
- [Calibration of 3D Single-pixel Imaging Systems with a Calibration Field](https://mlsanity.com/paper/2410.07545/) (2024)
- [Multi-Task Deep Learning for Surface Metrology](https://mlsanity.com/paper/2510.20339/) (2025)
- [Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry](https://mlsanity.com/paper/2607.11928/) (2026)

## Abstract (arXiv)

In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.