Zhiyong Xu
Chinese Academy of Sciences
11 Papers
12 Citations
Zhiyong Xu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Iterative reconstruction. The author has an hindex of 5, co-authored 11 publications. Previous affiliations of Zhiyong Xu include The Institute of Optics.
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Papers
Dim small target detection based on high-order cumulant of motion estimation
Xiangsuo Fan,Xiangsuo Fan,Zhiyong Xu,Jianlin Zhang,Yongmei Huang,Zhenming Peng,Wei Ziran,Wei Ziran,Hongwei Guo +8 more
TL;DR: Experiments show that the proposed ring perturbation correction method can effectively improve the local signal-to-noise ratio (SNR) of dim small targets; in sequence image detection, the proposed algorithm achieves better detection results than other algorithms, and can effectively detectdim small targets in strong clutter background.
25
Dim small targets detection based on self-adaptive caliber temporal-spatial filtering
TL;DR: Targets’ multi-frame movement correlation in the time-space domain is combined with the scale-space theory to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets’ scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the target.
24
Shallow Graph Convolutional Network for Skeleton-Based Action Recognition.
TL;DR: Wang et al. as mentioned in this paper proposed a plug-and-play channel adaptive merging module (CAMM), which can merge the vertices from the same part of the skeleton graph adaptively and efficiently.
20
Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel.
TL;DR: Wang et al. as discussed by the authors proposed a plug-and-play method for global motion estimation based on feature points, which has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.
12
Improving the Signal-to-Noise Ratio of Superresolution Imaging Based on Single-Pixel Camera
TL;DR: A new algorithm from convex optimization approximately to nonconvex optimization algorithm is proposed and compared with the traditional image reconstruction algorithm, such as greedy pursuit algorithm, minimum norm algorithm, and image interpolation, the peak signal-to-noise ratio and imaging quality of reconstructed images are effectively improved.