The binary defocusing technique is sensitive to the defocusing degree. The defocusing projection mechanism will introduce high-frequency harmonics at the inappropriate defocused level, leading to limitations in measurement accuracy and depth range. In this paper, a binary-focusing projection technique combining generative adversarial networks is proposed. First, the focusing binary patterns based on error diffusion are projected on the measured surface, and then the captured fringe patterns are input to generative adversarial networks, which achieves sinusoidal correction and optimization for both the focused region and the low-quality defocused region due to its strong image translation ability. Finally, 3D measurement is realized by a phase-shifting algorithm. Compared with the traditional binary defocusing technique, the proposed method is not limited by the defocusing degree and maintains the advantages of high-speed projection, so it can achieve a larger measured depth range and improve measurement accuracy. Simulation and experiments verify the performance of the proposed method.
Fringe projection profilometry (FPP) is one of popular 3D measurement techniques, which can be divided into two categories: Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). Compared to FTP, PSP has been increasingly appealing to researchers due to its merits of higher accuracy and sensitivity. Although PSP works well with the assumption that the object stays quasi-static, it is sensitive to the object motion and causes the motion-induced error in dynamic measurement due to the multi-frame measurement mechanism. However, the multi-frequency phase unwrapping is always utilized to solve the problem of surface discontinuities, which limits the reduction of numbers of projected patterns. Besides, using the high-speed camera will increase the cost of the hardware. Recently, researchers have demonstrated that the phase unwrapping, fringe denoising and dynamic range can be improved with the assistance of deep learning technique. Therefore, in this paper, a deep learning-based method is proposed for motion-induced error reduction. With the aid of strong fitting capability of the neural network, the motion-induced errors can be significantly reduced even under low capture frame rate. The proposed method is experimentally verified on its applicability for dynamic 3D measurement.
Depth-resolved wavenumber-scanning interferometry (DRWSI) is used to measure the contours or displacement fields inside a structure. One of the most promising phase retrieving algorithms of DRWSI is the eigenvalue decomposition and least-squares algorithm, because it can blindly evaluate the number of interferometric sources with a fine depth resolution. However, it is not robust to noise, in particular, salt noise or impulse noise. In order to significantly improve the immunity of DRWSI to noise, an updated eigenvalue decomposition algorithm is developed in this manuscript by employing the Spearman’s rank (SR) correlation function as a kernel function. Extreme experimental conditions under an environment with salt noise are designed to verify the performance of the new algorithm in DRWSI, called eigenvalue decomposition using SR correlation and Fourier transform. The results show that it is very effective for the phase reconstruction of DRWSI.
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