no code implementations • 18 Nov 2023 • Yixiao Yang, Ran Tao, Kaixuan Wei, Jun Shi
The realm of classical phase retrieval concerns itself with the arduous task of recovering a signal from its Fourier magnitude measurements, which are fraught with inherent ambiguities.
no code implementations • 7 Aug 2023 • Kaixuan Wei, Xiao Li, Johannes Froech, PRANEETH CHAKRAVARTHULA, James Whitehead, Ethan Tseng, Arka Majumdar, Felix Heide
The explosive growth of computation and energy cost of artificial intelligence has spurred strong interests in new computing modalities as potential alternatives to conventional electronic processors.
1 code implementation • 27 Apr 2023 • Linwei Chen, Ying Fu, Kaixuan Wei, Dezhi Zheng, Felix Heide
Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments.
1 code implementation • 17 Sep 2022 • 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 whenever the specifics of task changes.
1 code implementation • 4 Aug 2021 • Kaixuan Wei, Ying Fu, Yinqiang Zheng, Jiaolong Yang
Enhancing the visibility in extreme low-light environments is a challenging task.
Ranked #4 on
Image Denoising
on SID SonyA7S2 x100
no code implementations • 23 Jul 2021 • Yixiao Yang, Ran Tao, Kaixuan Wei, Ying Fu
In this paper, a dynamic proximal unrolling network (dubbed DPUNet) was proposed, which can handle a variety of measurement matrices via one single model without retraining.
1 code implementation • 18 Nov 2020 • Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb
In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.
1 code implementation • CVPR 2020 • Kaixuan Wei, Ying Fu, Jiaolong Yang, Hua Huang
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.
Ranked #5 on
Image Denoising
on ELD SonyA7S2 x200
2 code implementations • 10 Mar 2020 • Kaixuan Wei, Ying Fu, Hua Huang
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum.
1 code implementation • ICML 2020 • Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
1 code implementation • CVPR 2019 • Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, Hua Huang
Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems.
Ranked #3 on
Reflection Removal
on SIR^2(Objects)