no code implementations • 17 Mar 2022 • Zhiyuan Zha, Bihan Wen, Xin Yuan, Saiprasad Ravishankar, Jiantao Zhou, Ce Zhu
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
no code implementations • 10 Jan 2022 • Lanqing Guo, Renjie Wan, Wenhan Yang, Alex Kot, Bihan Wen
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e. g., real noise.
no code implementations • 11 Nov 2021 • Lanqing Guo, Siyu Huang, Haosen Liu, Bihan Wen
One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements.
no code implementations • 15 Oct 2021 • Tao Bai, Jun Zhao, Lanqing Guo, Bihan Wen
Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment.
1 code implementation • 26 Sep 2021 • Hao Cheng, YuFei Wang, Haoliang Li, Alex C. Kot, Bihan Wen
In this work, we propose a novel Disentangled Feature Representation framework, dubbed DFR, for few-shot learning applications.
no code implementations • 10 Sep 2021 • Jiafei Duan, Samson Yu, Soujanya Poria, Bihan Wen, Cheston Tan
However, there is a lack of intuitive physics models that are tested on long physical interaction sequences with multiple interactions among different objects.
Ranked #1 on
Semantic Object Interaction Classification
on SPACE
1 code implementation • 13 Jul 2021 • Rongkai Zhang, Lanqing Guo, Siyu Huang, Bihan Wen
Low-light image enhancement (LLIE) is a pervasive yet challenging problem, since: 1) low-light measurements may vary due to different imaging conditions in practice; 2) images can be enlightened subjectively according to diverse preferences by each individual.
no code implementations • 12 Jul 2021 • Rongkai Zhang, Jiang Zhu, Zhiyuan Zha, Justin Dauwels, Bihan Wen
To benchmark the effectiveness of reinforcement learning in R3L, we train a recurrent neural network with the same architecture for residual recovery using the deterministic loss, thus to analyze how the two different training strategies affect the denoising performance.
1 code implementation • 1 Jun 2021 • Hao Cheng, Kim-Hui Yap, Bihan Wen
Recent image classification algorithms, by learning deep features from large-scale datasets, have achieved significantly better results comparing to the classic feature-based approaches.
no code implementations • 1 Mar 2021 • Way Tan, Bihan Wen, Xulei Yang
StyleGAN is one of the state-of-the-art image generators which is well-known for synthesizing high-resolution and hyper-realistic face images.
no code implementations • 2 Feb 2021 • Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, Qian Wang
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.
no code implementations • 14 Jan 2021 • Yanjun Li, Bihan Wen, Hao Cheng, Yoram Bresler
In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of different modalities or data from distinct types of entities.
no code implementations • 21 Sep 2020 • Tao Bai, Jinnan Chen, Jun Zhao, Bihan Wen, Xudong Jiang, Alex Kot
In this paper, we propose a novel approach called Guided Adversarial Contrastive Distillation (GACD), to effectively transfer adversarial robustness from teacher to student with features.
no code implementations • 2 Aug 2020 • Xuankai Liu, Fengting Li, Bihan Wen, Qi Li
In this paper, we benchmark the robustness of watermarking, and propose a novel backdoor-based watermark removal framework using limited data, dubbed WILD.
1 code implementation • 17 Jul 2020 • Siyu Huang, Haoyi Xiong, Zhi-Qi Cheng, Qingzhong Wang, Xingran Zhou, Bihan Wen, Jun Huan, Dejing Dou
Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e. g., appearance, pose, foreground, background, local details, global structures, etc.
no code implementations • 14 Jul 2020 • Hao Cheng, Joey Tianyi Zhou, Wee Peng Tay, Bihan Wen
Graph Neural Networks (GNN) has demonstrated the superior performance in many challenging applications, including the few-shot learning tasks.
1 code implementation • 24 Jun 2020 • Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar, Bihan Wen
Experimental results demonstrate that (1) Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed multi-modality image restoration scheme achieves superior denoising results in both efficiency and effectiveness on RGB-NIR images.
no code implementations • 14 Jun 2020 • Zerui Shao, Yi-Fei PU, Jiliu Zhou, Bihan Wen, Yi Zhang
Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally varying (i. e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse.
no code implementations • 16 May 2020 • Zhiyuan Zha, Xin Yuan, Joey Tianyi Zhou, Jiantao Zhou, Bihan Wen, Ce Zhu
In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.
no code implementations • 29 Mar 2020 • Bihan Wen, Yanjun Li, Yuqi Li, Yoram Bresler
Furthermore, we relate the denoising performance improvement by combining multiple models, to the image model relationships.
1 code implementation • 17 Mar 2020 • Siyu Huang, Haoyi Xiong, Tianyang Wang, Bihan Wen, Qingzhong Wang, Zeyu Chen, Jun Huan, Dejing Dou
This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs.
2 code implementations • 22 May 2019 • Sicheng Wang, Bihan Wen, Junru Wu, DaCheng Tao, Zhangyang Wang
Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step.
no code implementations • 25 Mar 2019 • Bihan Wen, Saiprasad Ravishankar, Luke Pfister, Yoram Bresler
The model could be pre-learned from datasets, or learned simultaneously with the reconstruction, i. e., blind CS (BCS).
1 code implementation • 6 Sep 2018 • Ding Liu, Bihan Wen, Jianbo Jiao, Xian-Ming Liu, Zhangyang Wang, Thomas S. Huang
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation.
no code implementations • 3 Aug 2018 • Bihan Wen, Yanjun Li, Yoram Bresler
Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications.
1 code implementation • 6 Jul 2018 • Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image.
1 code implementation • NeurIPS 2018 • Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang
The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.
Ranked #1 on
Grayscale Image Denoising
on Urban100 sigma50
1 code implementation • 3 Oct 2017 • Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.
1 code implementation • ICCV 2017 • Bihan Wen, Yanjun Li, Luke Pfister, Yoram Bresler
In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT.
no code implementations • 12 Sep 2017 • Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Sparse coding has achieved a great success in various image processing tasks.
2 code implementations • 14 Jun 2017 • Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas S. Huang
Conventionally, image denoising and high-level vision tasks are handled separately in computer vision.
no code implementations • 16 Aug 2016 • Zhiyuan Zha, Bihan Wen, Jiachao Zhang, Jiantao Zhou, Ce Zhu
Inspired by enhancing sparsity of the weighted L1-norm minimization in comparison with L1-norm minimization in sparse representation, we thus explain that WNNM is more effective than NMM.
no code implementations • 9 Jun 2016 • Soumyabrata Dev, Bihan Wen, Yee Hui Lee, Stefan Winkler
Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere.
1 code implementation • journals 2016 • Ding Liu, Zhaowen Wang, Bihan Wen, Student Member, Jianchao Yang, Member, Wei Han, and Thomas S. Huang, Fellow, IEEE
We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.
no code implementations • 19 Nov 2015 • Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision.