The crosstalk problem of holography-based modal wavefront sensing (HMWS) becomes more severe with increasing aberration. In this paper, crosstalk effects on the sensor response are analyzed statistically for typical aberrations due to atmospheric turbulence. For specific turbulence strength, we optimized the sensor by adjusting the detector radius and the encoded phase bias for each Zernike mode. Calibrated response curves of low-order Zernike modes were further utilized to improve the sensor accuracy. The simulation results validated our strategy. The number of iterations for obtaining a residual RMS wavefront error of 0.1λ is reduced from 18 to 3.
Pavement macrotexture is one of the major factors affecting pavement functions, and it is meaningful to reconstruct the pavement macrotexture rapidly and accurately for pavement life cycle performance and quality evaluation. To reconstruct pavement macrotexture from monocular image, a novel method was developed based on a deep convolutional neural network (CNN). First, the red-greenblue (RGB) images and depth maps (RGB-D) of pavement texture were acquired by smartphone and laser texture scanner, respectively, from various asphalt mixture slab specimens fabricated in the laboratory, and the pavement texture RGB-D dataset was established from scratch. Then, an encoder-decoder CNN architecture was proposed based on residual network-101, and different training strategies were discussed for model optimization. Finally, the precision of the CNN and the three-dimensional characteristics of the reconstructed macrotexture were analyzed. The results show that the established RGB-D dataset can be used for training directly, and the established CNN architecture is plausible and effective. The mean texture depth and f 8mac of the reconstructed macrotexture both correlate with the benchmarks significantly, and the correlation coefficients are 0.88 and 0.96, respectively. It could be concluded that the proposed CNN can reconstruct the macrotexture from monocular RGB images precisely, and the reconstructed macrotexture could be further used for pavement macrotexture evaluation.
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