Xin Yuan

Chengdu University of Traditional Chinese Medicine

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Lawrence Carin

King Abdullah University of Science and Technology

David J. Brady

University of Arizona

Zhiyuan Zha

Nanyang Technological University

Bihan Wen

Nanyang Technological University

Ce Zhu

University of Electronic Science and Technology of China

Jiantao Zhou

University of Macau

Yulun Zhang

ETH Zurich

Ziyi Meng

Kuaishou (China)

Jinli Suo

Tsinghua University

Qionghai Dai

Tsinghua University

Cooperative Institutions

Centro Brasileiro de Pesquisas Físicas

455

European Organization for Nuclear Research

414

University of Chinese Academy of Sciences

240

Centre National de la Recherche Scientifique

225

Tsinghua University

225

Chinese Academy of Sciences

189

University of Zurich

184

Peking University

179

École Polytechnique Fédérale de Lausanne

158

Universidade Federal do Rio de Janeiro

152

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Various Total Variation for Snapshot Video Compressive Imaging

arXiv (Cornell University) (2020)

Xin Yuan

Sampling high-dimensional images is challenging due to limited availability of sensors; scanning is usually necessary in these cases. To mitigate this challenge, snapshot compressive imaging (SCI) was proposed to capture the high-dimensional (usually 3D) images using a 2D sensor (detector). Via novel optical design, the {\em measurement} captured by the sensor is an encoded image of multiple frames of the 3D desired signal. Following this, reconstruction algorithms are employed to retrieve the high-dimensional data. Though various algorithms have been proposed, the total variation (TV) based method is still the most efficient one due to a good trade-off between computational time and performance. This paper aims to answer the question of which TV penalty (anisotropic TV, isotropic TV and vectorized TV) works best for video SCI reconstruction? Various TV denoising and projection algorithms are developed and tested for video SCI reconstruction on both simulation and real datasets.

Snapshot (computer storage)

Variation (astronomy)

10.48550/arxiv.2005.08028

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Citations (4)

Learn to Unlearn: A Survey on Machine Unlearning

arXiv (Cornell University) (2023)

Youyang Qu Xin Yuan Ming Ding Wei Ni Thierry Rakotoarivelo

Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on removing the influence of specific data samples from a trained ML model. Such efficient removal would enable ML to comply with the "right to be forgotten" in many legislation, and could also address performance bottlenecks from low-quality or poisonous samples. In that context, machine unlearning methods have been proposed to erase the contributions of designated data samples on models, as an alternative to the often impracticable approach of retraining models from scratch. This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We further highlight emerging challenges and prospective research directions (e.g. resilience and fairness concerns). We aim for this paper to provide valuable resources for integrating privacy, equity, andresilience into ML systems and help them "learn to unlearn".

Retraining

10.48550/arxiv.2305.07512

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Citations (8)

Deep Equilibrium Models for Video Snapshot Compressive Imaging

arXiv (Cornell University) (2022)

Yaping Zhao Siming Zheng Xin Yuan

The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data has led to an inverse problem, which consists of recovering the HD signal from the compressed and noisy measurement. While reconstruction algorithms grow fast to solve it with the recent advances of deep learning, the fundamental issue of accurate and stable recovery remains. To this end, we propose deep equilibrium models (DEQ) for video SCI, fusing data-driven regularization and stable convergence in a theoretically sound manner. Each equilibrium model implicitly learns a nonexpansive operator and analytically computes the fixed point, thus enabling unlimited iterative steps and infinite network depth with only a constant memory requirement in training and testing. Specifically, we demonstrate how DEQ can be applied to two existing models for video SCI reconstruction: recurrent neural networks (RNN) and Plug-and-Play (PnP) algorithms. On a variety of datasets and real data, both quantitative and qualitative evaluations of our results demonstrate the effectiveness and stability of our proposed method. The code and models are available at: https://github.com/IndigoPurple/DEQSCI .

Snapshot (computer storage)

Regularization

Signal Recovery

10.48550/arxiv.2201.06931

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Citations (10)

Supplementary Data from Susceptibility-Associated Genetic Variation in <i>NEDD9</i> Contributes to Prostate Cancer Initiation and Progression

Dong Han Jude Owiredu Bridget M. Healy Mu‐Qing Li Maryam Labaf

<p>This document contains supplementary Figure 1-6</p>

Variation (astronomy)

10.1158/0008-5472.22429026

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Downlink and Uplink Cooperative Joint Communication and Sensing

arXiv (Cornell University) (2022)

Xu Chen Zhiyong Feng Zhiqing Wei J. Andrew Zhang Xin Yuan

Downlink (DL) and uplink (UL) joint communication and sensing (JCAS) technologies have been individually studied for realizing sensing using DL and UL communication signals, respectively. Since the spatial environment and JCAS channels in the consecutive DL and UL JCAS time slots are generally unchanged, DL and UL JCAS may be jointly designed to achieve better sensing performance. In this paper, we propose a novel DL and UL cooperative (DUC) JCAS scheme, including a unified multiple signal classification (MUSIC)-based JCAS sensing scheme for both DL and UL JCAS and a DUC JCAS fusion method. The unified MUSIC JCAS sensing scheme can accurately estimate AoA, range, and Doppler based on a unified MUSIC-based sensing module. The DUC JCAS fusion method can distinguish between the sensing results of the communication user and other dumb targets. Moreover, by exploiting the channel reciprocity, it can also improve the sensing and channel state information (CSI) estimation accuracy. Extensive simulation results validate the proposed DUC JCAS scheme. It is shown that the minimum location and velocity estimation mean square errors of the proposed DUC JCAS scheme are about 20 dB lower than those of the state-of-the-art separated DL and UL JCAS schemes.

Channel state information

SIGNAL (programming language)

10.48550/arxiv.2211.04065

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Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging

arXiv (Cornell University) (2020)

Wei He Naoto Yokoya Xin Yuan

Coded aperture snapshot spectral imaging (CASSI) is a promising technique to capture the three-dimensional hyperspectral image (HSI) using a single coded two-dimensional (2D) measurement, in which algorithms are used to perform the inverse problem. Due to the ill-posed nature, various regularizers have been exploited to reconstruct the 3D data from the 2D measurement. Unfortunately, the accuracy and computational complexity are unsatisfied. One feasible solution is to utilize additional information such as the RGB measurement in CASSI. Considering the combined CASSI and RGB measurement, in this paper, we propose a new fusion model for the HSI reconstruction. We investigate the spectral low-rank property of HSI composed of a spectral basis and spatial coefficients. Specifically, the RGB measurement is utilized to estimate the coefficients, meanwhile the CASSI measurement is adopted to provide the orthogonal spectral basis. We further propose a patch processing strategy to enhance the spectral low-rank property of HSI. The proposed model neither requires non-local processing or iteration, nor the spectral sensing matrix of the RGB detector. Extensive experiments on both simulated and real HSI dataset demonstrate that our proposed method outperforms previous state-of-the-art not only in quality but also speeds up the reconstruction more than 5000 times.

10.48550/arxiv.2012.15104

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Citations (7)

Modeling Mask Uncertainty in Hyperspectral Image Reconstruction

Lecture notes in computer science (2022)

Jiamian Wang Yulun Zhang Xin Yuan Ziyi Meng Zhiqiang Tao

Hyperparameter

10.1007/978-3-031-19800-7_7

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Citations (7)

Principles for a video fire detection system

Fire Safety Journal (1999)

Xiaofang Cheng Jian Wu Xin Yuan Hao Zhou

Fire Detection

Video monitoring

Video camera

10.1016/s0379-7112(98)00047-2

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Citations (15)

Group Sparsity Residual Constraint With Non-Local Priors for Image Restoration

IEEE Transactions on Image Processing (2020)

Zhiyuan Zha Xin Yuan Bihan Wen Jiantao Zhou Ce Zhu

Group sparse representation (GSR) has made great strides in image restoration producing superior performance, realized through employing a powerful mechanism to integrate the local sparsity and nonlocal self-similarity of images. However, due to some form of degradation (e.g., noise, down-sampling or pixels missing), traditional GSR models may fail to faithfully estimate sparsity of each group in an image, thus resulting in a distorted reconstruction of the original image. This motivates us to design a simple yet effective model that aims to address the above mentioned problem. Specifically, we propose group sparsity residual constraint with nonlocal priors (GSRC-NLP) for image restoration. Through introducing the group sparsity residual constraint, the problem of image restoration is further defined and simplified through attempts at reducing the group sparsity residual. Towards this end, we first obtain a good estimation of the group sparse coefficient of each original image group by exploiting the image nonlocal self-similarity (NSS) prior along with self-supervised learning scheme, and then the group sparse coefficient of the corresponding degraded image group is enforced to approximate the estimation. To make the proposed scheme tractable and robust, two algorithms, i.e., iterative shrinkage/thresholding (IST) and alternating direction method of multipliers (ADMM), are employed to solve the proposed optimization problems for different image restoration tasks. Experimental results on image denoising, image inpainting and image compressive sensing (CS) recovery, demonstrate that the proposed GSRC-NLP based image restoration algorithm is comparable to state-of-the-art denoising methods and outperforms several testing image inpainting and image CS recovery methods in terms of both objective and perceptual quality metrics.

Inpainting

10.1109/tip.2020.3021291

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Citations (95)

Simultaneous Nonlocal Low-Rank And Deep Priors For Poisson Denoising

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Zhiyuan Zha Bihan Wen Xin Yuan Jiantao Zhou Ce Zhu

Poisson noise is a common electronic noise, which has widely occurred in various photo-limited imaging systems. However, due to signal-dependent and multiplicative characteristics for Poisson noise, Poisson denoising is still an open problem. In this paper, we propose a novel approach using simultaneous nonlocal low-rank and deep priors (SNLDP) for Poisson denoising. The proposed SNLD-P simultaneously employs nonlocal self-similarity and deep image priors under the hybrid plug and play framework, which comprises multiple pairs of complementary priors, namely, nonlocal and local, shallow and deep, and internal and external. To make the optimization tractable, an effective alternating direction method of multiplier (ADMM) algorithm under the alternative minimization framework is provided to solve the proposed SNLDP-based Poisson denoising problem. Experimental results demonstrate the superiority of the proposed SNLDP over many popular or state-of-the-art Poisson denoising algorithms in terms of quantitative and visual perception.

Minification

10.1109/icassp43922.2022.9746870

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Citations (8)