Search Results for author: Peng Liu

Found 74 papers, 25 papers with code

Indeterminate Probability Neural Network

1 code implementation21 Mar 2023 Tao Yang, Chuang Liu, Xiaofeng Ma, Weijia Lu, Ning Wu, Bingyang Li, Zhifei Yang, Peng Liu, Lin Sun, Xiaodong Zhang, Can Zhang

Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced.


In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks

no code implementations8 Mar 2023 Mengguan Pan, Shengheng Liu, Peng Liu, Wangdong Qi, Yongming Huang, Wang Zheng, Qihui Wu, Markus Gardill

Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas.


Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems

1 code implementation7 Feb 2023 Peng Liu, Lemei Zhang, Jon Atle Gulla

The emergency of Pre-trained Language Models (PLMs) has achieved tremendous success in the field of Natural Language Processing (NLP) by learning universal representations on large corpora in a self-supervised manner.

Language Modelling Recommendation Systems

Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning

no code implementations20 Jan 2023 Lan Zhang, Chen Cao, Zhilong Wang, Peng Liu

The Bidirectional Encoder Representations from Transformers (BERT) were proposed in the natural language process (NLP) and shows promising results.

Representation Learning

Link-level simulator for 5G localization

1 code implementation26 Dec 2022 Xinghua Jia, Peng Liu, Wangdong Qi, Shengheng Liu, Yongming Huang, Wang Zheng, Mengguan Pan, Xiaohu You

Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks.

Recommending on graphs: a comprehensive review from a data perspective

no code implementations23 Dec 2022 Lemei Zhang, Peng Liu, Jon Atle Gulla

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS).

Fairness Graph Learning +2

Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning

1 code implementation24 Oct 2022 Shijie Han, Siyuan Li, Bo An, Wei Zhao, Peng Liu

In this work, we develop a novel identity detection reinforcement learning (IDRL) framework that allows an agent to dynamically infer the identities of nearby agents and select an appropriate policy to accomplish the task.

Multi-agent Reinforcement Learning reinforcement-learning +1

Nowhere to Hide: A Lightweight Unsupervised Detector against Adversarial Examples

no code implementations16 Oct 2022 Hui Liu, Bo Zhao, Kehuan Zhang, Peng Liu

In this paper, we propose an AutoEncoder-based Adversarial Examples (AEAE) detector, that can guard DNN models by detecting adversarial examples with low computation in an unsupervised manner.

Precision measurement of the return distribution property of the Chinese stock market index

no code implementations18 Sep 2022 Peng Liu, Yanyan Zheng

(2) The central part of return distribution is well described by the symmetrical L\'{e}vy $\alpha$-stable process with a stability parameter comparable with the value of about 1. 4 extracted in the U. S. stock market.

Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning

no code implementations17 Aug 2022 Lin Ding, Peng Liu, Wenfeng Shen, Weijia Lu, Shengbo Chen

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning.

Few-Shot Learning

Learning-based Autonomous Channel Access in the Presence of Hidden Terminals

no code implementations7 Jul 2022 Yulin Shao, Yucheng Cai, Taotao Wang, Ziyang Guo, Peng Liu, Jiajun Luo, Deniz Gunduz

We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion.

Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations

no code implementations20 Jun 2022 Mengguan Pan, Peng Liu, Shengheng Liu, Wangdong Qi, Yongming Huang, Xiaohu You, Xinghua Jia, XiaoDong Li

Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing.

Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification

no code implementations24 May 2022 Liang Xu, Yi Cheng, Fan Zhang, Bingxuan Wu, Pengfei Shao, Peng Liu, Shuwei Shen, Peng Yao, Ronald X. Xu

This loss is effective in addressing quantity imbalances and outliers, while regulating the focus of attention on samples with varying classification difficulties.

Classification imbalanced classification +1

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

1 code implementation19 May 2022 Yang Xiang, Zhihua Wu, Weibao Gong, Siyu Ding, Xianjie Mo, Yuang Liu, Shuohuan Wang, Peng Liu, Yongshuai Hou, Long Li, Bin Wang, Shaohuai Shi, Yaqian Han, Yue Yu, Ge Li, Yu Sun, Yanjun Ma, dianhai yu

We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning.

Cross-Lingual Natural Language Inference Distributed Computing +2

Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation

no code implementations Findings (NAACL) 2022 Yujie Xing, Jinglun Cai, Nils Barlaug, Peng Liu, Jon Atle Gulla

Furthermore, we propose Domain-specific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word for a specific corpus compared to other corpora.

Response Generation

Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic

no code implementations8 Apr 2022 Peng Liu, Yanyan Zheng

(1) The distributions of the numbers for cumulative confirmed cases and deaths obey power-law in early stages of COVID-19 and stretched exponential function in subsequent course.

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

1 code implementation ICLR 2022 Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, Zhaoran Wang

We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL.

D4RL Offline RL +2

Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

no code implementations4 Jan 2022 Hui Liu, Bo Zhao, Yuefeng Peng, Weidong Li, Peng Liu

Experimental results show that the contribution of image transformations to adversarial detection is significantly different, the combination of them can significantly improve the generic detection ability against state-of-the-art adversarial attacks.

Multi-Modality Distillation via Learning the teacher's modality-level Gram Matrix

no code implementations21 Dec 2021 Peng Liu

It is necessary to force the student network to learn the modality relationship information of the teacher network.

Knowledge Distillation

Dynamic Bottleneck for Robust Self-Supervised Exploration

1 code implementation NeurIPS 2021 Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang

Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards.

CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

1 code implementation5 Oct 2021 Peng Liu, Charlie T. Tran, Bin Kong, Ruogu Fang

The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge.

Unsupervised Domain Adaptation

Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder

no code implementations30 Sep 2021 Zichuan Chen, Peng Liu

VAE, or variational auto-encoder, compresses data into latent attributes, and generates new data of different varieties.

Data Augmentation Image Classification

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain

no code implementations14 Sep 2021 Jianye Hao, Tianpei Yang, Hongyao Tang, Chenjia Bai, Jinyi Liu, Zhaopeng Meng, Peng Liu, Zhen Wang

In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks.

Autonomous Vehicles Efficient Exploration +3

Circuit design and integration feasibility of a high-resolution broadband on-chip spectral monitor

no code implementations12 Aug 2021 Mehedi Hasan, Gazi Mahamud Hasan, Houman Ghorbani, Mohammad Rad, Peng Liu, Eric Bernier, Trevor Hall

Full tuning of the comb of resonances over a free spectral range is achieved with a high-resolution bandwidth of 1. 30 GHz.

An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization

no code implementations28 Jul 2021 Fan Zhang, Bo Pan, Pengfei Shao, Peng Liu, Shuwei Shen, Peng Yao, Ronald X. Xu

In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions related to the disease from sMRI data.

Data Augmentation

Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features

no code implementations19 Jul 2021 Hui Liu, Bo Zhao, Minzhi Ji, Yuefeng Peng, Jiabao Guo, Peng Liu

In this paper, we reveal that imperceptible adversarial examples are the product of recessive features misleading neural networks, and an adversarial attack is essentially a kind of method to enrich these recessive features in the image.

Adversarial Attack

Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection

1 code implementation14 May 2021 Cangning Fan, Fangyi Zhang, Peng Liu, Xiuyu Sun, Hao Li, Ting Xiao, Wei Zhao, Xianglong Tang

In this way, an obvious gap can be produced between the distributions of normal and abnormal data in the target domain, therefore enabling the anomaly detection in the domain.

Anomaly Detection

Principled Exploration via Optimistic Bootstrapping and Backward Induction

1 code implementation13 May 2021 Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang

In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I).

Efficient Exploration Reinforcement Learning (RL)

Ion-beam-assisted characterization of quinoline-insoluble particles in nuclear graphite

no code implementations11 Mar 2021 Qing Huang, Xinqing Han, Peng Liu, Jianjian Li, Guanhong Lei, Cheng Li

A much higher concentration of QI particles in NBG-18 than IG-110 was characterized and is suggested to be responsible for the smaller maximum dimensional shrinkage of NBG-18 than IG-110 during irradiation.

Applied Physics

VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention

no code implementations12 Feb 2021 Peng Liu, Yuewen Cao, Songxiang Liu, Na Hu, Guangzhi Li, Chao Weng, Dan Su

This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism, which refines the textual-to-acoustic alignment layer-wisely.

Speech Synthesis Text-To-Speech Synthesis

Security and Privacy for Artificial Intelligence: Opportunities and Challenges

no code implementations9 Feb 2021 Ayodeji Oseni, Nour Moustafa, Helge Janicke, Peng Liu, Zahir Tari, Athanasios Vasilakos

The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies.

Federated Learning

Sojourn times of Gaussian related random fields

no code implementations27 Jan 2021 Krzysztof Dȩbicki, Enkelejd Hashorva, Peng Liu, Zbigniew Michna

In the literature, based on the pioneering research of S. Berman the sojourn times have been utilised to derive the tail asymptotics of supremum of Gaussian processes.

Gaussian Processes Probability Primary 60G15, secondary 60G70

A Survey on Active Deep Learning: From Model-driven to Data-driven

no code implementations25 Jan 2021 Peng Liu, Lizhe Wang, Guojin He, Lei Zhao

Which samples should be labelled in a large data set is one of the most important problems for trainingof deep learning.

Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning

no code implementations1 Jan 2021 Chenjia Bai, Lingxiao Wang, Peng Liu, Zhaoran Wang, Jianye Hao, Yingnan Zhao

However, such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning (DRL).

Atari Games Efficient Exploration +3

Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder

1 code implementation27 Dec 2020 Lun-Pin Yuan, Peng Liu, Sencun Zhu

One of the most challenging problems in the field of intrusion detection is anomaly detection for discrete event logs.

Anomaly Detection Intrusion Detection

Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks

no code implementations23 Dec 2020 Qingtian Zou, Anoop Singhal, Xiaoyan Sun, Peng Liu

Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics.

Cryptography and Security

Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning

no code implementations17 Oct 2020 Chenjia Bai, Peng Liu, Kaiyu Liu, Lingxiao Wang, Yingnan Zhao, Lei Han, Zhaoran Wang

Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded.

Efficient Exploration reinforcement-learning +2

GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack

1 code implementation14 Oct 2020 Hui Liu, Bo Zhao, Minzhi Ji, Peng Liu

Adversarial examples are well-designed input samples, in which perturbations are imperceptible to the human eyes, but easily mislead the output of deep neural networks (DNNs).

Adversarial Attack

Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection

1 code implementation11 Sep 2020 Lan Zhang, Peng Liu, Yoon-Ho Choi, Ping Chen

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective.

Malware Detection reinforcement-learning +1

Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews

no code implementations9 Sep 2020 Ying Chen, Peng Liu, Chung Piaw Teo

Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews.

Classification General Classification +3

Ordering and Inequalities for Mixtures on Risk Aggregation

no code implementations24 Jul 2020 Yuyu Chen, Peng Liu, Yang Liu, Ruodu Wang

Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation.

Speaker Independent and Multilingual/Mixlingual Speech-Driven Talking Head Generation Using Phonetic Posteriorgrams

no code implementations20 Jun 2020 Huirong Huang, Zhiyong Wu, Shiyin Kang, Dongyang Dai, Jia Jia, Tianxiao Fu, Deyi Tuo, Guangzhi Lei, Peng Liu, Dan Su, Dong Yu, Helen Meng

Recent approaches mainly have following limitations: 1) most speaker-independent methods need handcrafted features that are time-consuming to design or unreliable; 2) there is no convincing method to support multilingual or mixlingual speech as input.

Talking Head Generation

Towards classification parity across cohorts

no code implementations16 May 2020 Aarsh Patel, Rahul Gupta, Mukund Harakere, Satyapriya Krishna, Aman Alok, Peng Liu

In this research work, we aim to achieve classification parity across explicit as well as implicit sensitive features.

Classification coreference-resolution +6

Email Threat Detection Using Distinct Neural Network Approaches

no code implementations LREC 2020 Esteban Castillo, Sreekar Dhaduvai, Peng Liu, Kartik-Singh Thakur, Adam Dalton, Tomek Strzalkowski

This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools.

BIG-bench Machine Learning

Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images

no code implementations17 Feb 2020 Peng Liu, Fuyu Li, Wanyi Li

To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images, and preserves the annotation information of the visible source domain.

Image-to-Image Translation object-detection +2

Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection

2 code implementations6 Jan 2020 Peng Liu, Ruogu Fang

In addition, we develop several attention strategies to guide the networks to learn the important features that have a major impact on prediction accuracy.


Using Deep Learning to Solve Computer Security Challenges: A Survey

no code implementations12 Dec 2019 Yoon-Ho Choi, Peng Liu, Zitong Shang, Haizhou Wang, Zhilong Wang, Lan Zhang, Junwei Zhou, Qingtian Zou

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community.

Cryptography and Security

Image Restoration Using Deep Regulated Convolutional Networks

1 code implementation19 Oct 2019 Peng Liu, Xiaoxiao Zhou, Junyi Yang, El Basha Mohammad D, Ruogu Fang

While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest.

Image Denoising Image Restoration +1

SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion

no code implementations18 Oct 2019 Peng Liu, Ruogu Fang

With substantial public concerns on potential cancer risks and health hazards caused by the accumulated radiation exposure in medical imaging, reducing radiation dose in X-ray based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests.

Image Denoising

CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

2 code implementations16 Oct 2019 Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang

Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.

Unsupervised Domain Adaptation

DurIAN: Duration Informed Attention Network For Multimodal Synthesis

3 code implementations4 Sep 2019 Chengzhu Yu, Heng Lu, Na Hu, Meng Yu, Chao Weng, Kun Xu, Peng Liu, Deyi Tuo, Shiyin Kang, Guangzhi Lei, Dan Su, Dong Yu

In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously.

Speech Synthesis

Maximizing Mutual Information for Tacotron

2 code implementations30 Aug 2019 Peng Liu, Xixin Wu, Shiyin Kang, Guangzhi Li, Dan Su, Dong Yu

End-to-end speech synthesis methods already achieve close-to-human quality performance.

Speech Synthesis

Learning Privately over Distributed Features: An ADMM Sharing Approach

no code implementations17 Jul 2019 Yaochen Hu, Peng Liu, Linglong Kong, Di Niu

Distributed machine learning has been widely studied in order to handle exploding amount of data.

SvTPM: A Secure and Efficient vTPM in the Cloud

1 code implementation21 May 2019 Juan Wang, Chengyang Fan, Jie Wang, Yueqiang Cheng, Yinqian Zhang, Wenhui Zhang, Peng Liu, Hongxin Hu

In this paper, we present SvTPM, a secure and efficient software-based vTPM implementation based on hardware-rooted Trusted Execution Environment (TEE), providing a whole life cycle protection of vTPMs in the cloud.

Cryptography and Security

Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications

no code implementations5 Oct 2018 Brian Paden, Peng Liu, Schuyler Cullen

Linear temporal logic and automaton-based run-time verification provide a powerful framework for designing task and motion planning algorithms for autonomous agents.

Autonomous Driving Decision Making +1

Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising

1 code implementation28 Jul 2017 Peng Liu, Ruogu Fang

In this work, we explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn pixel-distribution from noisy data.

Image Denoising

Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior

2 code implementations17 Jul 2017 Peng Liu, Ruogu Fang

We explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn similar pixel-distribution features from noisy images.

Image Denoising

Hey, you, keep away from my device: remotely implanting a virus expeller to defeat Mirai on IoT devices

no code implementations19 Jun 2017 Chen Cao, Le Guan, Peng Liu, Neng Gao, Jingqiang Lin, Ji Xiang

In particular, at a negotiated time slot, a customer is required to reboot the compromised device, then a "white" Mirai operated by the manufacturer breaks into the clean-state IoT devices immediately.

Cryptography and Security

Active Deep Learning for Classification of Hyperspectral Images

no code implementations30 Nov 2016 Peng Liu, HUI ZHANG, Kie B. Eom

It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.

Active Learning Classification +3

GaDei: On Scale-up Training As A Service For Deep Learning

no code implementations18 Nov 2016 Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bo-Wen Zhou, Fei Wang

By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.

Context-aware System Service Call-oriented Symbolic Execution of Android Framework with Application to Exploit Generation

no code implementations2 Nov 2016 Lannan Luo, Qiang Zeng, Chen Cao, Kai Chen, Jian Liu, Limin Liu, Neng Gao, Min Yang, Xinyu Xing, Peng Liu

We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework.

Cryptography and Security Software Engineering

Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks

no code implementations6 Oct 2016 Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong

Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.

Autonomous Vehicles Dimensionality Reduction +2

High-speed real-time single-pixel microscopy based on Fourier sampling

no code implementations15 Jun 2016 Qiang Guo, Hongwei Chen, Yuxi Wang, Yong Guo, Peng Liu, Xiurui Zhu, Zheng Cheng, Zhenming Yu, Minghua Chen, Sigang Yang, Shizhong Xie

However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time.

Image Reconstruction Image Restoration

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