Search Results for author: Huangxing Lin

Found 7 papers, 2 papers with code

Self-Verification in Image Denoising

no code implementations1 Nov 2021 Huangxing Lin, Yihong Zhuang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley

Specifically, we treat the output of the network as a ``prior'' that we denoise again after ``re-noising''.

Image Denoising

Adaptive noise imitation for image denoising

no code implementations30 Nov 2020 Huangxing Lin, Yihong Zhuang, Yue Huang, Xinghao Ding, Yizhou Yu, Xiaoqing Liu, John Paisley

Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.

Image Denoising

Hard Class Rectification for Domain Adaptation

1 code implementation8 Aug 2020 Yunlong Zhang, Changxing Jing, Huangxing Lin, Chaoqi Chen, Yue Huang, Xinghao Ding, Yang Zou

Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.

Unsupervised Domain Adaptation

Noise2Blur: Online Noise Extraction and Denoising

no code implementations3 Dec 2019 Huangxing Lin, Weihong Zeng, Xinghao Ding, Xueyang Fu, Yue Huang, John Paisley

Using the new image pair, the denoising network learns to generate clean and high-quality images from noisy observations.

Image Denoising

Learning Rate Dropout

2 code implementations30 Nov 2019 Huangxing Lin, Weihong Zeng, Xinghao Ding, Yue Huang, Chenxi Huang, John Paisley

The uncertainty of the descent path helps the model avoid saddle points and bad local minima.

Rain O'er Me: Synthesizing real rain to derain with data distillation

no code implementations9 Apr 2019 Huangxing Lin, Yanlong Li, Xinghao Ding, Weihong Zeng, Yue Huang, John Paisley

We present a supervised technique for learning to remove rain from images without using synthetic rain software.

Rain Removal

A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal

no code implementations24 Nov 2018 Huangxing Lin, Xueyang Fu, Changxing Jing, Xinghao Ding, Yue Huang

Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens.

Rain Removal

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