Abstract: The trichromatic mask single-color camera 3D PIV technology uses an optical trichromatic mask element to modulate the imaging light path of a color camera, combining color information with perspective information. This enables the RGB channels of the color-sensitive chip to record images of tracer particles from three perspectives, achieving single-camera 3D flow field measurements. Color cameras typically use a single sensor with a Bayer mask to capture color images. To obtain complete three-channel information of tracer particles, the missing color components in the Bayer image must be restored through demosaicing to extract the three-perspective images. This paper employs High Quality Linear Interpolation (HQLI) algorithms, Gradient Based Threshold Free (GBTF) algorithms, and deep learning algorithms based on U-Net + + neural networks to demosaic particle Bayer images. The quality of three-perspective image extraction by these three algorithms is evaluated using image evaluation metrics and the impact on particle reconstruction quality Q. Simulation experiments on artificially synthesized Gaussian vortex 3D flow fields are conducted to analyze the impact of these algorithms on measurement error. Zero-Net Mass Flux (ZNMF) jet experiments are utilized to study the demosaicing of experimental particle images and analyze the results of transient velocity fields. The results demonstrate that, compared to traditional algorithms (HQLI, GBTF), the deep learning algorithm based on U-Net++ neural networks can more effectively extract three-perspective images of particles, thereby reducing measurement error.