一种结合增强对齐与多注意力融合的偏振视频超分辨方法

In polarization imaging detection, polarization multi-channel video sequence brings challenges to data transmission and processing, while detection accuracy depends on high-resolution images. Therefore, super resolution computing imaging becomes the key technology of polarization imaging detection system. A polarization video super-resolution reconstruction method is proposed based on enhanced alignment and multi-attention fusion. A three-level cascade structure is adopted to achieve feature alignment. Each level of features is obtained by twice downsampling of the upper level features, and feature alignment is performed by using deformable convolution guided by optical flow; In the fusion stage, multiple attention mechanisms, including temporal attention, spatial attention and channel attention, are combined to make full use of inter channel dependency and spatiotemporal correlation to improve the effect of super-resolution reconstruction, qualitative and quantitative comparative experiments were conducted between the method proposed and other video super-resolution methods, such as VSR-DUF, RBPN, EDVR, RLSP, RSDN, RRN, DARN, MSAWN, on 0% 45°, 90°, 135°polarized videos. Experiments show that the method outperforms other algorithms in terms of peak signal-to-noise ratio and structural similarity metrics, and that the high-frequency texture detail restoration of video frames is richer and closer to the original HD image frames from the polarization imaging system.