Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging

Abstract

Video Snapshot Compressive Imaging (SCI) is a new imaging method based on compressive sensing. It encodes image sequences into a single snapshot measurement and then recovers the original high-speed video through reconstruction algorithms, which has the advantages of a low hardware cost and high imaging efficiency. How to construct an efficient algorithm is the key problem of video SCI. Although the current mainstream deep convolution network reconstruction methods can directly learn the inverse reconstruction mapping, they still have shortcomings in the representation of the complex spatiotemporal content of video scenes and the modeling of long-range contextual correlation. The quality of reconstruction still needs to be improved. To solve this problem, we propose a Transformer-based Cascading Reconstruction Network for Video Snapshot Compressive Imaging. In terms of the long-range correlation matching in the Transformer, the proposed network can effectively capture the spatiotemporal correlation of video frames for reconstruction. Specifically, according to the residual measurement mechanism, the reconstruction network is configured as a cascade of two stages: overall structure reconstruction and incremental details reconstruction. In the first stage, a multi-scale Transformer module is designed to extract the long-range multi-scale spatiotemporal features and reconstruct the overall structure. The second stage takes the measurement of the first stage as the input and employs a dynamic fusion module to adaptively fuse the output features of the two stages so that the cascading network can effectively represent the content of complex video scenes and reconstruct more incremental details. Experiments on simulation and real datasets show that the proposed method can effectively improve the reconstruction accuracy, and ablation experiments also verify the validity of the constructed network modules.

Keywords

• video compressive sensing
• convolutional neural network
• transformer
• cascading reconstruction
• residual measurement