Search Results for author: Bihan Wen

Found 58 papers, 27 papers with code

SinSR: Diffusion-Based Image Super-Resolution in a Single Step

1 code implementation23 Nov 2023 YuFei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C. Kot, Bihan Wen

Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference.

Image Super-Resolution

Frequency Convergence of Complexon Shift Operators (Extended Version)

no code implementations12 Sep 2023 Purui Zhang, Xingchao Jian, Feng Ji, Wee Peng Tay, Bihan Wen

We recall the notion of a complexon as the limit of a simplicial complex sequence.

ExposureDiffusion: Learning to Expose for Low-light Image Enhancement

1 code implementation ICCV 2023 YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C. Kot, Bihan Wen

Different from a vanilla diffusion model that has to perform Gaussian denoising, with the injected physics-based exposure model, our restoration process can directly start from a noisy image instead of pure noise.

Image Denoising Low-Light Image Enhancement

Enhancing Low-Light Images Using Infrared-Encoded Images

no code implementations9 Jul 2023 Shulin Tian, YuFei Wang, Renjie Wan, Wenhan Yang, Alex C. Kot, Bihan Wen

In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum.

Low-Light Image Enhancement

Beyond Learned Metadata-based Raw Image Reconstruction

1 code implementation21 Jun 2023 YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C. Kot, Bihan Wen

Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features.

Image Compression Image Reconstruction +1

Denoising Diffusion Models for Plug-and-Play Image Restoration

1 code implementation15 May 2023 Yuanzhi Zhu, Kai Zhang, Jingyun Liang, JieZhang Cao, Bihan Wen, Radu Timofte, Luc van Gool

Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.

Deblurring Denoising +4

Towards Adversarially Robust Continual Learning

no code implementations31 Mar 2023 Tao Bai, Chen Chen, Lingjuan Lyu, Jun Zhao, Bihan Wen

Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world.

Adversarial Robustness Continual Learning

Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation

no code implementations18 Mar 2023 Xiyu Wang, Yuecong Xu, Jianfei Yang, Bihan Wen, Alex C. Kot

The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors.

Autonomous Driving Self-Knowledge Distillation +1

Benchmarking White Blood Cell Classification Under Domain Shift

1 code implementation3 Mar 2023 Satoshi Tsutsui, Zhengyang Su, Bihan Wen

Recognizing the types of white blood cells (WBCs) in microscopic images of human blood smears is a fundamental task in the fields of pathology and hematology.

Benchmarking Classification

Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation

no code implementations3 Mar 2023 Ziwang Xu, Lanqing Guo, Shuyan Zhang, Alex C. Kot, Bihan Wen

In this work, we propose a novel unsupervised deep learning framework for the digital staining of cell images using knowledge distillation and generative adversarial networks (GANs).

Colorization Knowledge Distillation +1

Raw Image Reconstruction with Learned Compact Metadata

1 code implementation CVPR 2023 YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex Kot, Bihan Wen

While raw images exhibit advantages over sRGB images (e. g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements.

Image Compression Image Reconstruction +1

Removing Image Artifacts From Scratched Lens Protectors

1 code implementation11 Feb 2023 YuFei Wang, Renjie Wan, Wenhan Yang, Bihan Wen, Lap-Pui Chau, Alex C. Kot

Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts.

JPEG Artifact Removal

ShadowFormer: Global Context Helps Image Shadow Removal

1 code implementation3 Feb 2023 Lanqing Guo, Siyu Huang, Ding Liu, Hao Cheng, Bihan Wen

It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions.

Image Shadow Removal Shadow Removal

sRGB Real Noise Synthesizing With Neighboring Correlation-Aware Noise Model

1 code implementation CVPR 2023 Zixuan Fu, Lanqing Guo, Bihan Wen

Modeling and synthesizing real noise in the standard RGB (sRGB) domain is challenging due to the complicated noise distribution.


Frequency Guidance Matters in Few-Shot Learning

no code implementations ICCV 2023 Hao Cheng, Siyuan Yang, Joey Tianyi Zhou, Lanqing Guo, Bihan Wen

Few-shot classification aims to learn a discriminative feature representation to recognize unseen classes with few labeled support samples.

Few-Shot Learning Metric Learning

Temporal Output Discrepancy for Loss Estimation-based Active Learning

no code implementations20 Dec 2022 Siyu Huang, Tianyang Wang, Haoyi Xiong, Bihan Wen, Jun Huan, Dejing Dou

Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.

Active Learning Image Classification +1

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning

1 code implementation5 Oct 2022 Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan L. Yuille, Zongwei Zhou

However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices.

Active Learning Contrastive Learning

Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning

1 code implementation13 Sep 2022 Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels

We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections.

REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration

no code implementations25 Jul 2022 Chong Wang, Rongkai Zhang, Saiprasad Ravishankar, Bihan Wen

To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks.

Deblurring Image Deblurring +4

ABCDE: An Agent-Based Cognitive Development Environment

no code implementations10 Jun 2022 Jieyi Ye, Jiafei Duan, Samson Yu, Bihan Wen, Cheston Tan

How can the most common 1, 000 concepts (89\% of everyday use) be learnt in a naturalistic children's setting?

Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing

1 code implementation17 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.

Compressive Sensing

Enhancing Low-Light Images in Real World via Cross-Image Disentanglement

no code implementations10 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.

Disentanglement Low-Light Image Enhancement

FINO: Flow-based Joint Image and Noise Model

no code implementations11 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.

Denoising Image Restoration

Adversarial Purification through Representation Disentanglement

no code implementations15 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.


Disentangled Feature Representation for Few-shot Image Classification

1 code implementation26 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.

Benchmarking Classification +3

Variational Disentanglement for Domain Generalization

no code implementations13 Sep 2021 YuFei Wang, Haoliang Li, Hao Cheng, Bihan Wen, Lap-Pui Chau, Alex C. Kot

Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain.

Disentanglement Domain Generalization +1

PIP: Physical Interaction Prediction via Mental Simulation with Span Selection

no code implementations10 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.

Friction Semantic Object Interaction Classification

ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement

1 code implementation13 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.

Low-Light Image Enhancement reinforcement-learning +2

R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery

no code implementations12 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.

Benchmarking Image Denoising +3

Reconciliation of Statistical and Spatial Sparsity For Robust Image and Image-Set Classification

1 code implementation1 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.

Classification Image Classification

Systematic Analysis and Removal of Circular Artifacts for StyleGAN

no code implementations1 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.

Recent Advances in Adversarial Training for Adversarial Robustness

no code implementations2 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.

Adversarial Robustness

Joint Dimensionality Reduction for Separable Embedding Estimation

no code implementations14 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.

feature selection Information Retrieval +3

Feature Distillation With Guided Adversarial Contrastive Learning

no code implementations21 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.

Adversarial Robustness Contrastive Learning

Removing Backdoor-Based Watermarks in Neural Networks with Limited Data

no code implementations2 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.

Data Augmentation

Generating Person Images with Appearance-aware Pose Stylizer

1 code implementation17 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.

Image Generation

Attentive Graph Neural Networks for Few-Shot Learning

no code implementations14 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.

Few-Shot Learning

Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration

1 code implementation24 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.

Denoising Image Reconstruction +1

Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection

no code implementations14 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.

Moving Object Detection object-detection +1

The Power of Triply Complementary Priors for Image Compressive Sensing

no code implementations16 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.

Compressive Sensing Image Restoration

A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems

no code implementations29 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.

Denoising Image Restoration

Parameter-Free Style Projection for Arbitrary Style Transfer

1 code implementation17 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.

Style Transfer

Segmentation-Aware Image Denoising without Knowing True Segmentation

2 code implementations22 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.

Image Denoising Image Restoration +2

Connecting Image Denoising and High-Level Vision Tasks via Deep Learning

1 code implementation6 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.

Image Denoising Vocal Bursts Intensity Prediction

The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling

no code implementations3 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.

Dictionary Learning Image Denoising +2

From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

1 code implementation6 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.

Image Compression Image Denoising +1

Non-Local Recurrent Network for Image Restoration

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.

Image Denoising Image Restoration +1

VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising

1 code implementation3 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.

Dictionary Learning Image Denoising +3

Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising

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.

Denoising Video Denoising

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

2 code implementations14 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.

Image Denoising

A Comparative Study for the Nuclear Norms Minimization Methods

no code implementations16 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.

Deblurring Dictionary Learning +2

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

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.

Image Super-Resolution

FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications

no code implementations19 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.

Denoising Dictionary Learning +1

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