Local Enhancement Reconstruction Algorithm Based on Multi-hypothesis Prediction in Compressed Video Sensing

In multi-hypothesis prediction-based compressed video sensing reconstruction algorithms, the matching degrees of the hypothesis set corresponding to different image blocks are quite different, so the reconstruction difficulty of different blocks is obviously different. In this paper, a local enhancement reconstruction algorithm based on multi-hypothesis (MH-LE) is proposed. Image blocks are classified into two categories and a pixel domain dual channel matching strategy is proposed for moving image blocks, where the basic features of the image blocks are enhanced to improve the matching effectivity of similar blocks and obtain a higher quality hypothesis set. Besides, the structural similarity evaluation criteria are introduced into the matching block weight assignment process to improve prediction accuracy. The simulation results show that the reconstruction quality of the proposed algorithm is superior to other multi-hypothesis prediction-based reconstruction algorithms. Compared with the group sparsity-based reconstruction algorithms, the proposed algorithm possesses faster reconstruction speed and higher reconstruction quality at most sampling rates. © 2022 Science Press. All rights reserved.