Search Results for author: Hua Huang

Found 29 papers, 13 papers with code

Physics-Inspired Degradation Models for Hyperspectral Image Fusion

no code implementations4 Feb 2024 Jie Lian, Lizhi Wang, Lin Zhu, Renwei Dian, Zhiwei Xiong, Hua Huang

To fill this gap, we propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI, which comprises a spatial degradation network (SpaDN) and a spectral degradation network (SpeDN).

CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning

no code implementations25 Jan 2024 Zheqi He, Xinya Wu, Pengfei Zhou, Richeng Xuan, Guang Liu, Xi Yang, Qiannan Zhu, Hua Huang

Current multi-modal benchmarks for domain-specific knowledge concentrate on multiple-choice questions and are predominantly available in English, which imposes limitations on the comprehensiveness of the evaluation.

Multiple-choice Position

Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency

no code implementations23 Dec 2023 Yu Cai, Tianyu Shen, Shi-Sheng Huang, Hua Huang

Depth completion, aiming to predict dense depth maps from sparse depth measurements, plays a crucial role in many computer vision related applications.

Depth Completion

In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging

no code implementations20 Dec 2023 Xin Wang, Lizhi Wang, Xiangtian Ma, Maoqing Zhang, Lin Zhu, Hua Huang

Dual-Camera Compressed Hyperspectral Imaging (DCCHI) offers the capability to reconstruct 3D Hyperspectral Image (HSI) by fusing compressive and Panchromatic (PAN) image, which has shown great potential for snapshot hyperspectral imaging in practice.

Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising

1 code implementation13 Oct 2023 Hansen Feng, Lizhi Wang, Yiqi Huang, Yuzhi Wang, Lin Zhu, Hua Huang

Specifically, we integrate physical priors into neural proxies and introduce three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution loss (DDL).

Image Denoising

Recurrent Spike-based Image Restoration under General Illumination

1 code implementation6 Aug 2023 Lin Zhu, Yunlong Zheng, Mengyue Geng, Lizhi Wang, Hua Huang

Spike camera is a new type of bio-inspired vision sensor that records light intensity in the form of a spike array with high temporal resolution (20, 000 Hz).

Denoising Image Reconstruction +1

SC-NeuS: Consistent Neural Surface Reconstruction from Sparse and Noisy Views

no code implementations12 Jul 2023 Shi-Sheng Huang, Zi-Xin Zou, Yi-Chi Zhang, Hua Huang

The recent neural surface reconstruction by volume rendering approaches have made much progress by achieving impressive surface reconstruction quality, but are still limited to dense and highly accurate posed views.

Surface Reconstruction

Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling

1 code implementation8 Jul 2023 Tong Li, Hansen Feng, Lizhi Wang, Zhiwei Xiong, Hua Huang

Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding.

Image Denoising

Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling

1 code implementation13 Jul 2022 Hansen Feng, Lizhi Wang, Yuzhi Wang, Hua Huang

Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream.

Image Denoising

Revisiting Competitive Coding Approach for Palmprint Recognition: A Linear Discriminant Analysis Perspective

no code implementations30 Jun 2022 Lingfei Song, Hua Huang

Based on our analysis, we examined the statistics of palmprints and concluded that CompCode deviates from the optimal condition.

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution

1 code implementation16 Nov 2021 Yuanfei Huang, Jie Li, Yanting Hu, Xinbo Gao, Hua Huang

Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples.

Image Super-Resolution

Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework

no code implementations31 Jul 2021 Li Ding, Yongwei Wang, Xin Ding, Kaiwen Yuan, Ping Wang, Hua Huang, Z. Jane Wang

Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images.

Adversarial Defense Image Classification +1

Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

1 code implementation29 Mar 2021 Yuanfei Huang, Jie Li, Yanting Hu, Xinbo Gao, Hua Huang

Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable degradation priors.

Blind Super-Resolution Super-Resolution

Snapshot Hyperspectral Imaging Based on Weighted High-order Singular Value Regularization

no code implementations22 Jan 2021 Niankai Cheng, Hua Huang, Lei Zhang, Lizhi Wang

In this paper, we propose an effective high-order tensor optimization based method to boost the reconstruction fidelity for snapshot hyperspectral imaging.

Vocal Bursts Intensity Prediction

TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems

1 code implementation18 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.

Denoising Retrieval

A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising

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.

Image Denoising

3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

2 code implementations10 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.

Hyperspectral Image Denoising Image Denoising

Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems

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.

Denoising Retrieval

Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection

no code implementations18 Oct 2019 Jun Zhou, Hua Huang, Bin Liu, Xiuping Liu

Then we use multi-task optimization to train the normal estimation and local plane classification tasks simultaneously. Also, to integrate the advantages of multi-scale results, a scale selection strategy is adopted, which is a data-driven approach for selecting the optimal scale around each point and encourages subnetwork specialization.

Playing Atari Ball Games with Hierarchical Reinforcement Learning

no code implementations27 Sep 2019 Hua Huang, Adrian Barbu

We argue that these instructions have tremendous value in designing a reinforcement learning system which can learn in human fashion, and we test the idea by playing the Atari games Tennis and Pong.

Atari Games Hierarchical Reinforcement Learning +3

Accelerated Variance Reduced Stochastic Extragradient Method for Sparse Machine Learning Problems

no code implementations25 Sep 2019 Fanhua Shang, Lin Kong, Yuanyuan Liu, Hua Huang, Hongying Liu

Moreover, our theoretical analysis shows that AVR-SExtraGD enjoys the best-known convergence rates and oracle complexities of stochastic first-order algorithms such as Katyusha for both strongly convex and non-strongly convex problems.

BIG-bench Machine Learning Face Recognition +1

Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements

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.

Reflection Removal

Image Restoration from Patch-based Compressed Sensing Measurement

no code implementations2 Jun 2017 Guangtao Nie, Ying Fu, Yinqiang Zheng, Hua Huang

A series of methods have been proposed to reconstruct an image from compressively sensed random measurement, but most of them have high time complexity and are inappropriate for patch-based compressed sensing capture, because of their serious blocky artifacts in the restoration results.

Compressive Sensing Image Reconstruction +1

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