35 citations · 96 across the 13 of their papers we have counts for
5 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…
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Yuanhao Cai, Xiaowan Hu, Haoqian Wang +3
Existing deep learning real denoising methods require a large amount of noisy-clean image pairs for supervision. Nonetheless, capturing a real noisy-clean dataset is an unacceptabl…
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…