222 citations · 604 across the 28 of their papers we have counts for
9 papers · 1 filter
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…
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution
Bin Sun, Yulun Zhang, Songyao Jiang +1
Convolutional neural network (CNN) has achieved great success on image super-resolution (SR). However, most deep CNN-based SR models take massive computations to obtain high perfor…
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…
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction
Jiamian Wang, Yulun Zhang, Xin Yuan +2
Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system. Exist…
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The HS…
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
Jiamian Wang, Yulun Zhang, Xin Yuan +2
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way. It is generally implemented by two components: an op…
Texture Hallucination for Large-Factor Painting Super-Resolution
Yulun Zhang, Zhifei Zhang, Stephen DiVerdi +3
We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e.g., 8X, 16X). However,…
FCN: Fully Channel-Concatenated Network for Single Image Super-Resolution
Xiaole Zhao, Ying Liao, Tian He +3
Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back pr…