In this paper, we present a parallel implementation of a fixed-point algorithm for finding the solution of the total variation model for phase demodulation. The total variation model is efficient in estimating discontinuous phase maps, background illumination, and amplitude modulation from a single fringe pattern. The implementations include execution in a multi-core CPU and a GPU using OpenMP and CUDA, respectively. We show performance comparisons of the parallel implementations with 64-bit and 32-bit precision floating-point numbers using synthetic and real experimental data. Results show that our parallel implementations achieve speedups over the serial implementation of 9x for multi-core CPU and 103x for GPU.

在本文中，我们提出了一种并行实现的定点算法，用于寻找相位解调的全变模型的解。总变差模型可以有效地从单个条纹图案估计不连续的相位图、背景照明和幅度调制。实现包括分别使用 OpenMP 和 CUDA 在多核 CPU 和 GPU 中执行。我们使用合成和实际实验数据展示了并行实现与 64 位和 32 位精度浮点数的性能比较。结果表明，我们的并行实现比多核 CPU 的 9 倍和 GPU 的 103 倍的串行实现实现了加速。