Binary deep prior guided video snapshot compressive imaging for full-field displacement measurement

High-speed full-field displacement measurement is constrained by the cost of high-speed cameras and the trade-off between frame rate and spatial resolution. Video snapshot compressive imaging (SCI) reconstructs multiple frames from a single exposure, offering an effective route to low-cost high-speed displacement measurement. Among SCI reconstruction methods, plug-and-play (PnP)-based methods offer favorable theoretical interpretability and flexibility, and therefore hold great promise for high-speed imaging applications such as photomechanical measurement. However, they typically adopt full-precision deep networks as the denoising prior, and this cost becomes significant in high-resolution, resource-constrained measurement systems. To address this issue, we propose a binary deep prior guided snapshot compressive imaging for full-field displacement measurement, termed BDPnP. Specifically, to the best of our knowledge, we first propose a lightweight deep video denoiser as an implicit prior based on binary convolutions, which significantly reduces the computational complexity and parameter count. Building upon this, a cross-measurement temporal collaboration strategy and a hybrid prior alternating strategy are proposed to alleviate temporal-context fragmentation at measurement boundaries and performance degradation under non-ideal hardware imaging, respectively. These two strategies further compensate for the information loss from binarization. Finally, we constructed an SCI hardware system and integrated BDPnP with a digital image correlation pipeline, using rigid-body translation as a benchmark case with exactly known ground truth. At 1024 × 1024 resolution with a compression ratio of 8, i.e., an eightfold gain in the effective acquisition frame rate, BDPnP achieves a mean displacement error of 0.045 mm, only 0.34% of the total displacement. This is comparable to the lowest mean error achieved by the fine-tuned deep learning method in our experiments (0.044 mm) while requiring only 1/47.7 of its FLOPs and 1/7.7 of its parameters. Our code is available at https://github.com/hnu-VML/qmx/tree/main/BDPnP.