63 citations · 156 across the 11 of their papers we have counts for
6 papers · 1 filter
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Jing Lin, Xiaowan Hu, Yuanhao Cai +5
How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised…
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Yuanhao Cai, Jing Lin, Haoqian Wang +5
In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a…
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Yuanhao Cai, Jing Lin, Zudi Lin +5
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image…
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D hyperspectral images (HSIs) from a 2D…
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
Xiaowan Hu, Yuanhao Cai, Jing Lin +5
The rapid development of deep learning provides a better solution for the end-to-end reconstruction of hyperspectral image (HSI). However, existing learning-based methods have two…
Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin, Yuanhao Cai, Xiaowan Hu +7
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring. However, CNN-based methods show limitations in capturing long-range…