Block-based compressive sensing of video using local sparsifying transform

Abstract

Block-based compressive sensing is attractive for sensing natural images and video because it makes large-sized image/video tractable. However, its reconstruction performance is yet to be improved much. This paper proposes a new block-based compressive video sensing recovery scheme which can reconstruct video sequences with high quality. It generates initial key frames by incorporating the augmented Lagrangian total variation with a nonlocal means filter which is well known for being good at preserving edges and reducing noise. Additionally, local principal component analysis (PCA) transform is employed to enhance the detailed information. The non-key frames are initially predicted by their measurements and reconstructed key frames. Furthermore, regularization with PCA transform-aided side information iteratively seeks better reconstructed solution. Simulation results manifest effectiveness of the proposed scheme.