• •

### 时域注意力特征对齐的视频压缩感知重构网络

1. 华南理工大学电子与信息学院，广东 广州 510640
• 收稿日期:2022-01-05 修回日期:2022-04-01
• 作者简介:魏志超 男，1996年出生，河南禹州人.现为华南理工大学电子与信息学院硕士研究生.主要研究方向为视频压缩感知.E‑mail：zcwei2306@outlook.com
杨春玲（通信作者） 女，1970年出生，河南新乡人.现为华南理工大学电子与信息学院博士生导师．主要研究方向为图像/视频压缩编码、图像质量评价.E‑mail：eeclyang@scut.edu.cn
• 基金资助:
广东省自然科学基金(2019A1515011949)

### Video Compressed Sensing Reconstruction Network Based on Temporal-Attention Feature Alignment

WEI Zhi-chao, YANG Chun-ling

1. School of Electronic and Information Engineering，South China University of Technology，Guangzhou，Guangdong 510640，China
• Received:2022-01-05 Revised:2022-04-01

Abstract:

The motion compensation methods of optical flow alignment and deformable convolution alignment adopted by the existing video compressed sensing reconstruction algorithms have problems such as error accumulation and limited information perception range, which greatly limit their effectiveness and practicability. In order to adaptively extract the global information of the reference frame without introducing extra parameters, this paper first proposes an innovative idea of using the attention mechanism to realize motion estimation and motion compensation in video compressed sensing reconstruction, and then designs the temporal-attention feature alignment network(TAFA-Net) for implementation. On this basis, a joint deep reconstruction network(JDR-TAFA-Net) is proposed to achieve high-performance reconstruction for non-key frames. First, the reference frames are adaptively aligned to the current non-key frame through TAFA-Net, and then a fusion network based on the auto-encoder is introduced to fully extract the relevant information from existing frames to further enhance the reconstruction quality of the non-key frames. Experimental results show that, compared with the state-of-the-art iterative optimization-based method SSIM-InterF-GSR, the proposed method can improve PSNR(Peak Signal to Noise Ratio) by 4.74dB, and compared with the state-of-the-art deep learning-based method STM-Net, the proposed method can improve PSNR by 0.64dB.