视频超分辨率(VSR)的任务是利用序列视频帧具有的时间连续性和空间相似性提高视频的分辨率.主流的VSR方法利用像素损失优化网络,导致生成的超分辨率(SR)重建结果边缘模糊、细节平滑.为此,提出了一个新的引入时空特征补偿和多特征鉴别器的端到端的VSR网络框架MC-PETGAN.该框架包括光流估计补偿网络和多特征鉴别生成对抗网络.光流估计补偿网络利用相邻视频帧之间的短时连续和内容相似性特征为多特征鉴别生成对抗网络提供有效丰富的细节信息;生成器与包括像素、边缘和纹理鉴别器的多特征鉴别器的对抗训练促使SR帧与高分辨率(HR)帧的像素、边缘和纹理趋于一致.大量公共数据集和监控视频数据的实验结果表明,该文方法能够有效提高视频SR结果的像素精度,并恢复出清晰的边缘和纹理,而且视觉感受愉快,感知指标有竞争力.

Video super-resolution (VSR) aims to generate high-resolution (HR) video by exploiting temporal consistency and contextual similarity of low-resolution (LR) video sequences. The key to improving the quality of VSR lies in accurate frame alignment and the feature fusion of adjacent frames. We propose a dual channel attention deep and shallow super-resolution network, which combines with HR optical flow compensation to construct an end-to-end VSR framework HOFADS-VSR (attention deep and shallow VSR network union HR optical flow compensation). HR optical flow calculated by spatiotemporal dependency of consecutive LR frames is used to compensate adjacent frames to implement accurate frame alignment. Deep and shallow channels with attention residual block restore small-scale detail features and large-scale contour features, respectively, and strengthen the rich features of global and local regions through weight adjustment. Extensive experiments have been performed to demonstrate the effectiveness and robustness of HOFADS-VSR. Comparative results on the Vid4, SPMC-12, and Harmonic-8 datasets show that our network not only achieves good performance on peak signal-to-noise ratio and structural similarity index but also the restored structure and texture have excellent fidelity. (C) 2021 Society of Photo-Optical Instrumentation Engineers

Human pose estimation has attracted enormous interest in the field of human action recognition. When the human pose is complex (such as pose distortion, pose reversal, etc.) or there is background interference (multi-target, shadow, etc.), the keypoints obtained by existing methods of human pose estimation often have incorrect positioning, category, and connection. This paper proposes a novel human pose estimation network KACNet via the keypoint association constraints. The Channel-1 of KACNet is constrained by the distance loss function to obtain the position of keypoints, and the Channel-2 of KACNet is constrained by the association loss function to obtain the relationship of keypoints. Then, the position and relationship of keypoints are fused by the weighted loss function to obtain the keypoints with accurate location, classification, and connection. Experiments on a large number of public datasets and Internet data show that our method can effectively suppress background interference to improve the accuracy of complex human pose estimation. Compared with state-of-the-art human pose estimation methods, the proposed methods can accurately locate, classify, and connect the human body keypoints robustly.