PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm.

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.

Frequency-shifting technique for pixelwise absolute phase retrieval.

In fringe projection profilometry, phase shifting (PS) is the most used technique for phase retrieval. However, it suffers from periodicity of sine wave in PS; the result is wrapped into [-π,π]; and additional phase unwrapping (PU) is necessary to retrieve the absolute phase. In this paper, a more general technique termed frequency shifting is proposed, based on which the behavior of periodicity is eliminated and absolute phase can be retrieved pixelwisely without any phase unwrapping. The effectiveness of the proposed technique was verified by extensive experimental results, and they demonstrate comparable performance with those of the traditional technique combining PS and PU even in only one step and less projection.

Photometric constraint for absolute phase unwrapping from single-frequency fringe patterns.

As a fundamental step in fringe projection profilometry, absolute phase unwrapping via single-frequency fringe patterns is still a challenging ill-posed problem, which attracts lots of interest in the research area. To solve the problem above, additional constraints were constructed, such as spatial smoothness constraint (SSC) in spatial phase unwrapping algorithm and viewpoint consistency constraint (VCC) in multi-view systems (e.g., stereo and light-field cameras). However, there still exists phase ambiguity in the unwrapping result based on SSC. Moreover, VCC-based methods rely on additional cameras or light-field cameras, which makes the system complicated and expensive. In this paper, we propose to construct a novel constraint directly from photometric information in captured image intensity, which has never been fully exploited in phase unwrapping. The proposed constraint, named photometric constraint (PC), provides a prospective constraint for absolute phase unwrapping from single-frequency fringe patterns without any additional cameras. Extensive experiments have been conducted for the validation of the proposed method, which achieved comparable performance with the state-of-the-art method, given a traditional camera-projector setup and single high-frequency fringe patterns.

Phase-modulation combined deflectometry for small defect detection.

Deflectometry has been widely used to detect defects on specular surfaces. However, it is still very challenging to detect defects on semispecular or diffuse surfaces because of the low contrast and low signal-to-noise ratio. To address this challenge, we proposed a phase-modulation combined method for accurate defect detection. Based on the phase and modulation of captured fringes, a dual-branch convolutional neural network is employed to simultaneously extract geometric and photometric features from the phase-shifting pattern sequence and modulation, which improves the defect detection performance significantly. Compared to state-of-the-art methods, we believe the results demonstrated the proposed method's effectiveness and capability to reduce false positives.

Micro-Frequency Shifting Projection Technique for Inter-Reflection Removal.

Inter-reflection removal is vital for complex-scene reconstruction. However, most methods assume that the tested surface is a diffuse, and are limited to removal of inter-reflection caused only by diffuse reflections. For all kinds of inter-reflections caused by diffuse and specular reflections, a micro-frequency shifting (MFS) projection technique is presented. Because the modulation variation with frequency in inter-reflection regions is larger than that of other regions, we use the MFS technique to detect inter-reflections, where patterns with specifically designed frequency-shifts and base frequencies are projected. Inter-reflections are detected through large variations in modulation, and removed using a regional-projection technique. Experimental results validate the effectiveness for diffuse and specular inter-reflection removal.

Error of image saturation in the structured-light method.

In the phase-measuring structured-light method, image saturation will induce large phase errors. Usually, by selecting proper system parameters (such as the phase-shift number, exposure time, projection intensity, etc.), the phase error can be reduced. However, due to lack of a complete theory of phase error, there is no rational principle or basis for the selection of the optimal system parameters. For this reason, the phase error due to image saturation is analyzed completely, and the effects of the two main factors, including the phase-shift number and saturation degree, on the phase error are studied in depth. In addition, the selection of optimal system parameters is discussed, including the proper range and the selection principle of the system parameters. The error analysis and the conclusion are verified by simulation and experiment results, and the conclusion can be used for optimal parameter selection in practice.