The binary encoding fringe projection profilometry has the potential of achieving high-speed 3-D measurement. Error diffusion is one of the most widely investigated binary encoding techniques. However, the existing error diffusion usually utilizes the same diffusion kernel to encode sinusoidal (S) fringe patterns without considering the fringe pitch-widths, leading to inevitable measurement error due to the demodulated phase error. In this article, a fringe binary encoding strategy based on genetic algorithm is proposed to reduce the phase error recovered by the temporal phase unwrapping (TPU) method. We design an optimization equation that combines phase error and intensity error to calculate the encoding error, which ensures that the optimized diffusion kernel is robust to defocusing changes. Both simulation and experiments have proved the effectiveness and accurateness of the proposed method. Comparative experiments indicate that the extracted phase error is greatly reduced for two types of extensively adopted TPU methods at different defocusing levels. Especially for multi wavelength method, the phase error can be reduced by up to 68%.

Digital fringe projection (DFP) with defocused binary fringe patterns has the ability to overcome the projector nonlinearity and achieve a high-speed 3D measurement. The Floyd–Steinberg (FS) dithering technique is one of the most commonly adopted binary fringe coding methods due to its relatively high measurement accuracy. Nevertheless, we found that the FS binary fringe would cause a fixed error in the recovered phase, which is proven to be invariable for various defocusing levels and various phase-shift steps according to the analysis of the phase error based on noise model of phase-shifting profilometry. It means that FS binary fringe would have a certain offset in space, compared with standard sinusoidal fringe, which is verified to be essentially constant for different fringe pitches through simulation and experiment. This offset would distort the 3D geometry of the tested target for monocular systems relying on triangulation, which needs to be compensated to improve 3D measurement accuracy. Experiments are presented to demonstrate the enhanced 3D result after compensation.

In this paper, a compact, cost-effective, and fast translational online-switchable phase-shifting fringe (TOPF) projector is designed and fabricated for high accuracy three-dimensional (3D) face imaging. Compared with the conventional mechanical projectors, the main difference is that it utilizes a translational approach instead of a rotational one to achieve a better balance in terms of size, speed, accuracy, and cost. To mitigate the inconsistency of the motor's step size and ensure the stability of phase-shifting, an optical encoder-based feedback control mechanism is employed. Additionally, to address the random phase shift errors induced by mechanical motion, a fast, generalized phase-shifting algorithm with unknown phase shifts (uPSAs) that can calculate arbitrary phase shifts is proposed. Finally, a 3D imaging system consisting of the TOPF projector and two cameras is constructed for experimental validation. The feasibility, effectiveness, and precision of our proposed method are substantiated through the reconstruction of a static facial model and a dynamic real face.

In recent years, Generative Adversarial Networks (GANs)-based illumination processing of facial images has made favorable achievements. However, some GANs-based illumination-processing methods only pay attention to the image quality and neglect the recognition accuracy, whereas others only crop partial face area and ignore the challenges to synthesize photographic face, background and hair when the original face image is under extreme illumination (Image under extreme illumination (extreme illumination conditions) means that we cannot see the texture and structure information clearly and most pixel values tend to 0 or 255.). Moreover, the recognition accuracy is low when the faces are under extreme illumination conditions. For these reasons, we present an elaborately designed architecture based on convolutional neural network and GANs for processing the illumination of facial image. We use ResBlock at the down-sampling stage in our encoder and adopt skip connections in our generator. This special design together with our loss can enhance the ability to preserve identity and generate high-quality images. Moreover, we use different convolutional layers of a pre-trained feature network to extract varisized feature maps, and then use these feature maps to compute loss, which is named multi-stage feature maps (MSFM) loss. For the sake of fairly evaluating our method against state-of-the-art models, we use four metrics to estimate the performance of illumination-processing algorithms. A variety of experimental data indicate that our method is superior to the previous models under various illumination challenges in illumination processing. We conduct qualitative and quantitative experiments on two datasets, and the experimental data indicate that our scheme obviously surpasses the state-of-the-art algorithms in image quality and identification accuracy.

Fringe-structured light measurement technology has garnered significant attention in recent years. To enhance measurement speed while maintaining a certain level of accuracy using binary fringe, this paper proposes a phase retrieval method with single-frame binary square wave fringe. The proposed method utilizes image denoising through deep learning to extract the phase, enabling the use of a trained image denoiser as a low-pass filter, which adaptively replaces the manual selection of the appropriate band-pass filter. The results demonstrate that this method achieves higher reconstruction accuracy than the traditional single-frame algorithm while preserving more object details.