97 citations · 226 across the 10 of their papers we have counts for
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Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Zeqiang Lai, Kaixuan Wei, Ying Fu
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenev…
Physics-based Noise Modeling for Extreme Low-light Photography
Kaixuan Wei, Ying Fu, Yinqiang Zheng +1
Enhancing the visibility in extreme low-light environments is a challenging task. Under nearly lightless condition, existing image denoising methods could easily break down due to…
Dynamic Proximal Unrolling Network for Compressive Imaging
Yixiao Yang, Ran Tao, Kaixuan Wei +1
Compressive imaging aims to recover a latent image from under-sampled measurements, suffering from a serious ill-posed inverse problem. Recently, deep neural networks have been app…
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising
Kaixuan Wei, Ying Fu, Jiaolong Yang +1
Lacking rich and realistic data, learned single image denoising algorithms generalize poorly to real raw images that do not resemble the data used for training. Although the proble…
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang +3
Plug-and-play (PnP) is a non-convex framework that combines ADMM or other proximal algorithms with advanced denoiser priors. Recently, PnP has achieved great empirical success, esp…