Multi-frame particle image enhancement based on spatiotemporal feature interaction
Yiming Cao, Changdong Yu, Pan Li, Chenyi Rong, Junpeng Zhu, Shuaiyu Bao · Physics of Fluids · 2025
Particle image velocimetry (PIV) estimates velocity fields by capturing particle movements in consecutive images, holding significant importance in marine and naval fields. Thus, acquiring high-quality multi-frame particle images via PIV estimation is crucial. However, current PIV image preprocessing methods only focus on enhancing two-frame particle image pairs, ignoring the spatiotemporal correlation in continuous multi-frame particle images. Considering complex noise during continuous particle image capture and spatiotemporal correlations between image pairs, we designed the spatiotemporal feature interaction network (SFINet) for particle image preprocessing. First, in terms of spatial features, given particles' tiny, non-uniform, and highly similar features, we use an encoder–decoder with the mixed feature enhancement module and the adaptive fine-grained channel attention module to extract, enhance, and reconstruct particle detail features. Second, in terms of temporal features, three-dimensional convolution layers leverage spatiotemporal correlations in continuous particle images for feature fusion, completing image enhancement. For real-world noise, we autonomously generated a challenging image enhancement dataset, including low contrast, Gaussian noise (various concentrations), light intensity noise, and real underwater interference backgrounds to simulate real underwater environments. The results show that the proposed SFINet can effectively address the noise interference in both synthetic and real images. On the test set, it achieves particle image enhancement with the average peak signal-to-noise ratio reaching 36.255 and structural similarity index measure hitting 0.9843 between the denoised and ground-truth images, demonstrating its remarkable performance in handling noise and improving image quality.