In structured light systems, the accuracy of measurement notably diminishes when assessing complex texture objects, especially encountering boundaries between various colors. To address this challenge, this paper meticulously analyzes and establishes an error model, elaborating the correlation between phase errors and the gradients of phase and gray-scale. Based on this analysis, a novel high-precision method is proposed for measuring complex texture objects via bidirectional fringe projection. This approach firstly leverages horizontal and vertical fringe projections to derive bidirectional phase information and calculates the angles between the tangent of the texture edges and the phase gradient. Subsequently, a refined temporal phase correction algorithm is formulated based on the epipolar matching algorithm and the devised error model, effectively mitigating numerical instability issues within the algorithm and significantly reducing errors of bidirectional phases. Ultimately, corrected point clouds are calculated based on bidirectional phases, and the obtained point clouds are merged to further diminish phase errors. Comparison experiments indicate that this method can reduce Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 65.74% and 67.75%, respectively. Compared to existing methods, it improves performance by 27.29% and 33.74%, respectively, demonstrating superior performance.

The coded-aperture snapshot spectral imaging (CASSI) system has garnered significant attention as a promising spectral snapshot-based imaging technique. However, several challenges persist in the development of CASSI systems: 1. High hardware costs. 2. The difficulty of balancing the performance and computational efficiency of the reconstruction network. In this paper, we propose a dual-camera mask-free CASSI system to replace the expensive encoding hardware, thereby reducing the overall cost of CASSI. To address the issues of a high number of parameters and computational demands associated with Transformer-based methods, we design a hierarchical attention-based spectral-spatial transformer. This approach involves embedding local features on both the spectral and spatial channels, while also leveraging global information from these channels to enhance the network’s global learning capabilities. Our experimental results demonstrate the superiority of the mask-free approach, achieving a PSNR of 37.9 dB in a single camera and 44.7 dB in dual cameras. Furthermore, experiments indicate that the mask-free system outperforms state-of-the-art methods while requiring lower computational and memory costs.