Z
Zaiping Lin
Researcher at National University of Defense Technology
Publications - 55
Citations - 983
Zaiping Lin is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 11, co-authored 38 publications receiving 365 citations.
Papers
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Proceedings ArticleDOI
Learning Parallax Attention for Stereo Image Super-Resolution
TL;DR: A parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations is introduced and a new and the largest dataset for stereo image SR is proposed.
Proceedings ArticleDOI
Exploring Sparsity in Image Super-Resolution for Efficient Inference
TL;DR: Wang et al. as mentioned in this paper explored the sparsity in image SR to improve inference efficiency of SR networks and developed a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
Journal ArticleDOI
Deep Video Super-Resolution Using HR Optical Flow Estimation
TL;DR: Wang et al. as mentioned in this paper proposed an end-to-end video super-resolution network to super-resolve both optical flows and images, which can exploit temporal dependency between consecutive frames.
Book ChapterDOI
Learning for Video Super-Resolution through HR Optical Flow Estimation
TL;DR: This paper proposes an end-to-end trainable video SR framework to super-resolve both images and optical flows and demonstrates that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.
Journal ArticleDOI
A Constrained Sparse Representation Model for Hyperspectral Anomaly Detection
TL;DR: A novel sparsity-based algorithm for anomaly detection in hyperspectral imagery based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood.