Maciej Trusiak

TL;DR: The proposed ASR-EFEMD technique has been tested using simulated and experimental data including complex closed fringe patterns with wide spatial frequency range and its superiority over the SR-FABEMD-HS as well as its robustness to fringe pattern noise, background illumination and modulation defects have been corroborated.

TL;DR: A high-speed 3D shape measurement technique based on the optimized composite fringe patterns and stereo-assisted structured light system that allows to effectively recover an accurate, unambiguous, and distortion-free 3D point cloud with only 4 projected patterns.

TL;DR: The presented SR-FABEMD-HS method is compared with other normalization techniques and its superiority, potential and robustness to high fringe density variations and the presence of noise, modulation and background illumination defects in analyzed fringe patterns has been corroborated.

TL;DR: It is demonstrated in this work that deep learning techniques can automatically realize TPU through supervised learning, which can substantially improve the unwrapping reliability compared with MF-TPU even under different types of error sources, e.g., intensity noise, low fringe modulation, projector nonlinearity, and motion artifacts.

TL;DR: The Hilbert-Huang transform based preprocessing is proposed to enhance fringe pattern phase demodulation by filtering out the spurious noise and background illumination and performing fringe normalization.