Dynamic phase measuring profilometry for rigid objects based on simulated annealing

Dynamic 3D phase-shifting profilometry based on a corner optical flow algorithm

Real-time 3D reconstruction has been applied in many fields, calling for many ongoing efforts to improve the speed and accuracy of the used algorithms. Phase shifting profilometry based on the Lucas-Kanade optical flow method is a fast and highly precise method to construct and display the three-dimensional shape of objects. However, in this method, a dense optical flow calculation is required for the modulation image corresponding to the acquired deformed fringe pattern, which consumes a lot of time and affects the real-time performance of 3D reconstruction and display. Therefore, this paper proposes a dynamic 3D phase shifting profilometry based on a corner optical flow algorithm to mitigate this issue. Therein, the Harris corner algorithm is utilized to locate the feature points of the measured object, so that the optical flow needs to calculate for only the feature points which, greatly reduces the amount of calculation time. Both our experiments and simulations show that our method improves the efficiency of pixel matching by four times and 3D reconstruction by two times.

Nonlinear error full-field compensation method for phase measuring profilometry

Phase measuring profilometry (PMP) has the highest measuring accuracy among structured light projection-based three-dimensional (3D) sensing methods. Due to their low-cost and high-resolution features, commercial projectors are extensively used in PMP, but they are all designed with a gamma effect purpose that considers the characteristics of human vision. Affected by the gamma effect, a set of phase-shifting sinusoidal deformed patterns captured in PMP may contain high-order harmonics which lead to nonlinear phase errors. Then, a novel nonlinear error full-field compensation method is proposed. First, the unwrapped phases modulated by the reference plane are measured several times, and their average phase is taken as the measured phase modulated by the reference plane to eliminate random errors as much as possible. Second, an expected phase plane is fitted from this average phase with the least-squares method. Third, the nonlinear phase error can be detected by subtracting the fitted expected phase from this average phase. Finally, the full-field look-up table (LUT) can be established between the nonlinear phase error and the measured phase. When an object is measured, the unwrapped phase modulated by the object is taken as the measured phase of the LUT, so the corresponding nonlinear phase error can be directly searched in the LUT. In this way, the full-field nonlinear phase error can be efficiently compensated. Experimental results show the feasibility and validity of the proposed method. The mean absolute error (MAE) can be improved from 0.48 mm to 0.06 mm, and the root mean square error (RMSE) can be improved from 0.55 mm to 0.07 mm.

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

High-precision 3D shape measurement of rigid moving objects based on the Hilbert transform

Phase-shifting profilometry (PSP) is a three-dimensional (3D) measurement method of point-to-point calculation. The consistency of object position is the prerequisite to ensure the successful application of PSP in moving objects. The position information of an object can be well characterized by the modulation patterns, and hence a high-quality modulation pattern is the guarantee of pixel-matching accuracy. In this paper, a generic modulation pattern enhancement method for rigid moving objects based on the Hilbert transform is proposed. First, the Hilbert transform is employed to suppress the zero-frequency components of the fringe pattern, and a hybrid digital filter window is applied to filter out the positive fundamental frequency components for a higher signal-to-noise ratio. Then the grid-based motion statistics for fast, ultra-robust feature correspondence algorithm is used to match the high-quality modulation patterns between two adjacent frames, and the object positions in the three deformed patterns are made consistent by image clipping. Finally, the three-step PSP is used to reconstruct the 3D shape of the measured object. Experimental results demonstrate that the proposed method can substantially improve the quality of the modulation pattern, achieve high-precision pixel matching, and ultimately reduce the motion-introduced phase error.