arXiv subjects

Jianhua Wang

Publications and source records attributed to Jianhua Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRS

Given the need to elucidate the mechanisms underlying illnesses and their treatment, as well as the lack of harmonization of acquisition and post-processing protocols among different magnetic resonance system vendors, this work is to determine if metabolite concentrations obtained from different sessions, machine models and even different vendors of 3 T scanners can be highly reproducible and be pooled for diagnostic analysis, which is very valuable for the research of rare diseases. Participants underwent magnetic resonance imaging (MRI) scanning once on two separate days within one week (one session per day, each session including two proton magnetic resonance spectroscopy (1H-MRS) scans with no more than a 5-minute interval between scans (no off-bed activity)) on each machine. were analyzed for reliability of within- and between- sessions using the coefficient of variation (CV) and intraclass correlation coefficient (ICC), and for reproducibility of across the machines using correlation coefficient. As for within- and between- session, all CV values for a group of all the first or second scans of a session, or for a session were almost below 20%, and most of the ICCs for metabolites range from moderate (0.4-0.59) to excellent (0.75-1), indicating high data reliability. When it comes to the reproducibility across the three scanners, all Pearson correlation coefficients across the three machines approached 1 with most around 0.9, and majority demonstrated statistical significance (P<0.01). Additionally, the intra-vendor reproducibility was greater than the inter-vendor ones.

stat.ML↗

arXiv2025

An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age/standard deviation = 27.4/3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively; Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel.

physics.med-ph↗

arXiv2025

Weighted Combination and Singular Spectrum Analysis Based Remote Photoplethysmography Pulse Extraction in Low-light Environments

Camera-based vital signs monitoring in recent years has attracted more and more researchers and the results are promising. However, a few research works focus on heart rate extraction under extremely low illumination environments. In this paper, we propose a novel framework for remote heart rate estimation under low-light conditions. This method uses singular spectrum analysis (SSA) to decompose the filtered signal into several reconstructed components. A spectral masking algorithm is utilized to refine the preliminary candidate components on the basis of a reference heart rate. The contributive components are fused into the final pulse signal. To evaluate the performance of our framework in low-light conditions, the proposed approach is tested on a large-scale multi-illumination HR dataset (named MIHR). The test results verify that the proposed method has stronger robustness to low illumination than state-of-the-art methods, effectively improving the signal-to-noise ratio and heart rate estimation precision. We further perform experiments on the PUlse RatE detection (PURE) dataset which is recorded under normal light conditions to demonstrate the generalization of our method. The experiment results show that our method can stably detect pulse rate and achieve comparative results. The proposed method pioneers a new solution to the remote heart rate estimation in low-light conditions.

eess.IV↗

arXiv2025

Unconventional topological Weyl-dipole phonon

A pair of Weyl points (WPs) with opposite Chern numbers ${\cal{C}}$ can exhibit an additional higher-order $Z_2$ topological charge, giving rise to the formation of a $Z_2$ Weyl dipole. Owing to the nontrivial topological charge, $Z_2$ Weyl dipoles should also appear in pairs, and the WPs within each $Z_2$ Weyl dipole can not be annihilated when meeting together. As a novel topological state, the topological Weyl-dipole phase (TWDP) has garnered significant attention, yet its realization in crystalline materials remains a challenge. Here, through first-principles calculations and theoretical analysis, we demonstrate the existence of the Weyl-dipole phase in the phonon spectra of the $P6_3$ type Y(OH)$_3$. Particularly, the Weyl dipole in this system is protected by a quantized quadrupole moment, and it distinguished from conventional Weyl dipole, as it comprises an unconventional charge-3 WP with ${\cal{C}}=-3$ and three conventional charge-1 WPs with ${\cal{C}}=1$. Consequently, the Weyl-dipole phase in Y(OH)$_3$ features unique two-dimensional (2D) sextuple-helicoid Fermi-arc states on the top and bottom surfaces, protected by the Chern number, as well as one-dimensional (1D) hinge states that connect the two Weyl dipoles along the side hinges, guaranteed by the quantized quadrupole moment. Our findings not only introduce a novel higher-order topological phase, but also promote Y(OH)$_3$ as a promising platform for exploring multi-dimensional boundaries and the interaction between first-order and second-order topologies.

cond-mat.mtrl-sci↗

arXiv2025

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning

Federated learning is an essential distributed model training technique. However, threats such as gradient inversion attacks and poisoning attacks pose significant risks to the privacy of training data and the model correctness. We propose a novel approach called SMTFL to achieve secure model training in federated learning without relying on trusted participants. To safeguard gradients privacy against gradient inversion attacks, clients are dynamically grouped, allowing one client's gradient to be divided to obfuscate the gradients of other clients within the group. This method incorporates checks and balances to reduce the collusion for inferring specific client data. To detect poisoning attacks from malicious clients, we assess the impact of aggregated gradients on the global model's performance, enabling effective identification and exclusion of malicious clients. Each client's gradients are encrypted and stored, with decryption collectively managed by all clients. The detected poisoning gradients are invalidated from the global model through a unlearning method. We present a practical secure aggregation scheme, which does not require trusted participants, avoids the performance degradation associated with traditional noise-injection, and aviods complex cryptographic operations during gradient aggregation. Evaluation results are encouraging based on four datasets and two models: SMTFL is effective against poisoning attacks and gradient inversion attacks, achieving an accuracy rate of over 95% in locating malicious clients, while keeping the false positive rate for honest clients within 5%. The model accuracy is also nearly restored to its pre-attack state when SMTFL is deployed.

cs.CR↗

arXiv2024

Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI

In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, limiting their synergistic potential. To address this, we propose SQNet, a dual-task network integrating Transformer and Convolutional Neural Network (CNN) components. SQNet features a T2-refine fusion decoder for quantitative analysis, leveraging global features from the Transformer, and a segmentation decoder with multiple local region supervision for enhanced accuracy. A tight coupling module aligns and fuses CNN and Transformer branch features, enabling SQNet to focus on myocardium regions. Evaluation on healthy controls (HC) and acute myocardial infarction patients (AMI) demonstrates superior segmentation dice scores (89.3/89.2) compared to state-of-the-art methods (87.7/87.9). T2 quantification yields strong linear correlations (Pearson coefficients: 0.84/0.93) with label values for HC/AMI, indicating accurate mapping. Radiologist evaluations confirm SQNet's superior image quality scores (4.60/4.58 for segmentation, 4.32/4.42 for T2 quantification) over state-of-the-art methods (4.50/4.44 for segmentation, 3.59/4.37 for T2 quantification). SQNet thus offers accurate simultaneous segmentation and quantification, enhancing cardiac disease diagnosis, such as AMI.

eess.IV↗

arXiv2024

GameVLM: A Decision-making Framework for Robotic Task Planning Based on Visual Language Models and Zero-sum Games

With their prominent scene understanding and reasoning capabilities, pre-trained visual-language models (VLMs) such as GPT-4V have attracted increasing attention in robotic task planning. Compared with traditional task planning strategies, VLMs are strong in multimodal information parsing and code generation and show remarkable efficiency. Although VLMs demonstrate great potential in robotic task planning, they suffer from challenges like hallucination, semantic complexity, and limited context. To handle such issues, this paper proposes a multi-agent framework, i.e., GameVLM, to enhance the decision-making process in robotic task planning. In this study, VLM-based decision and expert agents are presented to conduct the task planning. Specifically, decision agents are used to plan the task, and the expert agent is employed to evaluate these task plans. Zero-sum game theory is introduced to resolve inconsistencies among different agents and determine the optimal solution. Experimental results on real robots demonstrate the efficacy of the proposed framework, with an average success rate of 83.3%.

cs.RO↗

arXiv2024

Real topological phonons in 3D carbon allotropes

There has been a significant focus on real topological systems that enjoy space-time inversion symmetry (PT ) and lack spin-orbit coupling. While the theoretical classification of the real topology has been established, more progress has yet to be made in the materials realization of such real topological systems in three dimensions (3D). To address this crucial issue, by selecting the carbon-based material candidates as targets, we perform high-throughput computing to inspect the real topology in the phonon spectrums of the 3D carbon allotropes in the Samara Carbon Allotrope Database (SACADA). Among 1192 kinds of 3D carbon allotropes, we find 65 real topological systems with a phononic real Chern insulating (PRCI) state, 2 real topological systems with a phononic real nodal line (PRNL) state, 10 real topological systems with a phononic real Dirac point (PRDP) state, and 8 real topological systems with a phononic real triple-point pair (PRTPP) state. This extremely expands the material candidates with real topology, especially for the gapless topological phonons. We exhibit the PRCI, PRNL, PRTPP, and PRDP states of 27-SG. 166-pcu-h, 1081-SG. 194- 4 2T13-CA, 52-SG. 141-gis, and 132-SG. 191-3,4T157 as illustrative examples, and explore the second-order boundary mode, i.e., phononic hinge mode. Among the four examples, the materials 1081-SG. 194-42T13-CA and 52-SG. 141-gis are so ideal that the PRNL and PRTPP in them are well separated from other bands, and the phononic hinge mode can be clearly observed. This study aims to broaden the understanding of 3D topological phonons, and emphasizes the potential of 3D carbon allotropes as a valuable framework for exploring the fascinating physics related to phononic hinge modes and phononic real topology.

cond-mat.mtrl-sci↗

arXiv2024

Microscale Hydrogen, Carbon, and Nitrogen Isotopic Diversity of Organic Matter in Asteroid Ryugu

We report the H, C, and N isotopic compositions of microscale (0.2 to 2$μ$m) organic matter in samples of asteroid Ryugu and the Orgueil CI carbonaceous chondrite. Three regolith particles of asteroid Ryugu, returned by the Hayabusa2 spacecraft, and several fragments of Orgueil were analyzed by NanoSIMS isotopic imaging. The isotopic distributions of the Ryugu samples from two different collection spots are closely similar to each other and to the Orgueil samples, strengthening the proposed Ryugu-CI chondrite connection. Most individual sub-$μ$m organic grains have isotopic compositions within error of bulk values, but 2-8% of them are outliers exhibiting large isotopic enrichments or depletions in D, $^{15}$N, and/or $^{13}$C. The H, C and N isotopic compositions of the outliers are not correlated with each other: while some C-rich grains are both D- and $^{15}$N-enriched, many are enriched or depleted in one or the other system. This most likely points to a diversity in isotopic fractionation pathways and thus diversity in the local formation environments for the individual outlier grains. The observation of a relatively small population of isotopic outlier grains can be explained either by escape from nebular and/or parent body homogenization of carbonaceous precursor material or addition of later isotopic outlier grains. The strong chemical similarity of isotopically typical and isotopically outlying grains, as reflected by synchrotron x-ray absorption spectra, suggests a genetic connection and thus favors the former, homogenization scenario. However, the fact that even the least altered meteorites show the same pattern of a small population of outliers on top of a larger population of homogenized grains indicates that some or most of the homogenization occurred prior to accretion of the macromolecular organic grains into asteroidal parent bodies.

astro-ph.EP↗

arXiv2024

A method of photometric data extraction for asteroids from time-domain surveys

The lightcurves of asteroids are essential for determining their physical characteristics, including shape, spin, size, and surface composition. However, most asteroids are missing some of these basic physical parameters due to lack of photometric data. Although a few telescopes or surveys are specially designed for photometric lightcurve observations of asteroids, many ground-based and space-based sky surveys for hunting new exoplanets, transient events, etc., should capture numerous small Solar System objects. This will benefit the physical studies of these objects. In order to extract data of these moving objects from time-domain photometric surveys, we have developed a new method using the model tree algorithm in the field of machine learning. A dedicated module is built to automatically identify moving objects in dataset, and extract their photometric and astrometric data. As the first application of this novel method, we have analyzed data in five fields of the Yunnan-Hong Kong wide field photometric (YNHK) survey, from which 538 lightcurves of 211 asteroids are successfully extracted. Meanwhile, we also tested the method based on the data from NASA's Transiting Exoplanet Survey Satellite, and the result proves the reliability of our method. With derived lightcurves of 13 asteroids from the YNHK survey, we have determined their synodic spin periods, among which the periods of 4 asteroids are estimated for the first time. In future, we are going to apply this method to search for small objects in the outer part of the Solar System from the Chinese Space Station Telescope survey.

astro-ph.EP↗

arXiv2023

Explosive Nucleosynthesis in Core-Collapse Type II Supernovae: Insights from new C, N, Si, and Al-Mg isotopic compositions of presolar grains

We report C, N, Si, and Al-Mg isotope data for 39 presolar X silicon carbide (SiC) and four silicon nitride grains - a group of presolar grains that condensed in the remnants of core-collapse Type II supernovae (CCSNe) - isolated from the Murchison meteorite. Energy dispersive X-ray (EDX) data were used to determine the Mg and Al contents of the X SiC grains for comparison with the Mg/Al ratios determined by secondary ion mass spectroscopy (SIMS). Previous SIMS studies have used O-rich standards in the absence of alternatives. In this study, the correlated isotopic and elemental data of the X SiC grains enabled accurate determination of the initial 26Al/27Al ratios for the grains. Our new grain data suggest that (i) the literature data for X grains are affected to varying degrees by asteroidal/terrestrial contamination, and (ii) the Al/Mg ratios in SiC are a factor of two (with +/-6% 1 sigma uncertainties) lower than estimated based on the SIMS analyses that used O-rich standards. The lowered Al/Mg ratios result in proportionally higher inferred initial 26Al/27Al ratios for presolar SiC grains. In addition, the suppression of asteroidal/terrestrial contamination in this study leads to the observation of negative trends for 12C/13C-30Si/28Si and 26Al/27Al-30Si/28Si among our CCSN grains. We discuss these isotope trends in the light of explosive CCSN nucleosynthesis models, based on which we provide new insights into several non-traditional CCSN nucleosynthesis processes, including explosive H burning, the existence of a C/Si zone in the outer regions of CCSNe, and neutrino-nucleus reactions in deep CCSN regions.

astro-ph.SR↗

arXiv2023

3D carbon allotropes: Topological quantum materials with obstructed atomic insulating phases, multiple bulk-boundary correspondences, and real topology

The study of topological phases with unconventional bulk-boundary correspondences and nontrivial real Chern number has garnered significant attention in the topological states of matter. Using the first-principle calculations and theoretical analysis, we perform a high-throughput material screening of the 3D obstructed atomic insulators (OAIs) and 3D real Chern insulators (RCIs) based on the Samara Carbon Allotrope Database (SACADA). Results show that 422 out of 703 3D carbon allotropes are 3D OAIs with multiple bulk-boundary correspondences, including 2D obstructed surface states (OSSs) and 1D hinge states, which are in one dimension and two dimensions lower than the 3D bulk, respectively. The 2D OSSs in these OAIs can be modified when subjected to appropriate boundaries, which benefits the investigation of surface engineering and the development of efficient topological catalysts. These 422 OAIs, which have 2D and 1D boundary states, are excellent platforms for multi-dimensional topological boundaries research. Remarkably, 138 of 422 OAIs are also 3D RCIs, which show a nontrivial real topology in the protection of spacetime inversion symmetry. Our work not only provides a comprehensive list of 3D carbon-based OAIs and RCIs, but also guides their application in various aspects based on multiple bulk-boundary correspondences and real topological phases.

cond-mat.mtrl-sci↗

arXiv2023

PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning

Recently, big data has seen explosive growth in the Internet of Things (IoT). Multi-layer FL (MFL) based on cloud-edge-end architecture can promote model training efficiency and model accuracy while preserving IoT data privacy. This paper considers an improved MFL, where edge layer devices own private data and can join the training process. iMFL can improve edge resource utilization and also alleviate the strict requirement of end devices, but suffers from the issues of Data Reconstruction Attack (DRA) and unacceptable communication overhead. This paper aims to address these issues with iMFL. We propose a Privacy Amplification scheme on iMFL (PA-iMFL). Differing from standard MFL, we design privacy operations in end and edge devices after local training, including three sequential components, local differential privacy with Laplace mechanism, privacy amplification subsample, and gradient sign reset. Benefitting from privacy operations, PA-iMFL reduces communication overhead and achieves privacy-preserving. Extensive results demonstrate that against State-Of-The-Art (SOTA) DRAs, PA-iMFL can effectively mitigate private data leakage and reach the same level of protection capability as the SOTA defense model. Moreover, due to adopting privacy operations in edge devices, PA-iMFL promotes up to 2.8 times communication efficiency than the SOTA compression method without compromising model accuracy.

cs.CR↗

arXiv2023

Correlation Cube Attack Revisited: Improved Cube Search and Superpoly Recovery Techniques

In this paper, we improve the cube attack by exploiting low-degree factors of the superpoly w.r.t. certain "special" index set of cube (ISoC). This can be viewed as a special case of the correlation cube attack proposed at Eurocrypt 2018, but under our framework more beneficial equations on the key variables can be obtained in the key-recovery phase. To mount our attack, one has two challenging problems: effectively recover algebraic normal form of the superpoly and extract out its low-degree factors; and efficiently search a large quantity of good ISoCs. We bring in new techniques to solve both of them. First, we propose the variable substitution technique for middle rounds of a cipher, in which polynomials on the key variables in the algebraic expressions of internal states are substituted by new variables. This will improve computational complexity of the superpoly recovery and promise more compact superpolys that can be easily decomposed with respect to the new variables. Second, we propose the vector numeric mapping technique, which seeks out a tradeoff between efficiency of the numeric mapping technique and accuracy of the monomial prediction technique in degree evaluation of superpolys. Combining with this technique, a fast pruning method is given and modeled by MILP to filter good ISoCs of which the algebraic degree satisfies some fixed threshold. Thanks to automated MILP solvers, it becomes practical to comprehensively search for good cubes across the entire search space. To illustrate the power of our techniques, we apply all of them to Trivium stream cipher. The previous best practical key recovery attack was on 820-round Trivium with complexity $2^{53.17}$. We put forward 820-, 825- and 830-round practical key-recovery attacks, in which there are 2^{80}\times 87.8%, 2^{80}\times 83% and 2^{80}\times 65.7% keys that could be practically recovered, respectively.

cs.CR↗

arXiv2023

Towards Runtime Customizable Trusted Execution Environment on FPGA-SoC

Processing sensitive data and deploying well-designed Intellectual Property (IP) cores on remote Field Programmable Gate Array (FPGA) are prone to private data leakage and IP theft. One effective solution is constructing Trusted Execution Environment (TEE) on FPGA-SoCs (FPGA System on Chips). Researchers have integrated this type TEE with Trusted Platform Module (TPM)-based trusted boot, denoted as FPGA-SoC tbTEE. But there is no effort on secure and trusted runtime customization of FPGA-SoC TEE. This paper extends FPGA-SoC tbTEE to build Runtime Customizable TEE (RCTEE) on FPGA-SoC by additive three major components (our work): 1) CrloadIP, which can load an IP core at runtime such that RCTEE can be adjusted dynamically and securely; 2) CexecIP, which can not only execute an IP core without modifying the operating system of FPGA-SoC TEE, but also prevent insider attacks from executing IPs deployed in RCTEE; 3) CremoAT, which can provide the newly measured RCTEE state and establish a secure and trusted communication path between remote verifiers and RCTEE. We conduct a security analysis of RCTEE and its performance evaluation on Xilinx Zynq UltraScale+ XCZU15EG 2FFVB1156 MPSoC.

cs.CR↗

arXiv2023

PASS: A Parameter Audit-based Secure and Fair Federated Learning Scheme against Free-Rider Attack

Federated Learning (FL) as a secure distributed learning framework gains interests in Internet of Things (IoT) due to its capability of protecting the privacy of participant data. However, traditional FL systems are vulnerable to Free-Rider (FR) attacks, which causes unfairness, privacy leakage and inferior performance to FL systems. The prior defense mechanisms against FR attacks assumed that malicious clients (namely, adversaries) declare less than 50% of the total amount of clients. Moreover, they aimed for Anonymous FR (AFR) attacks and lost effectiveness in resisting Selfish FR (SFR) attacks. In this paper, we propose a Parameter Audit-based Secure and fair federated learning Scheme (PASS) against FR attack. PASS has the following key features: (a) prevent from privacy leakage with less accuracy loss; (b) be effective in countering both AFR and SFR attacks; (c) work well no matter whether AFR and SFR adversaries occupy the majority of clients or not. Extensive experimental results validate that PASS: (a) has the same level as the State-Of-The-Art method in mean square error against privacy leakage; (b) defends against AFR and SFR attacks in terms of a higher defense success rate, lower false positive rate, and higher F1-score; (c) is still effective where adversaries exceed 50%, with F1-score 89% against AFR attack and F1-score 87% against SFR attack. Note that PASS produces no negative effect on FL accuracy when there is no FR adversary.

cs.CR↗

arXiv2023

CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning

Federated Learning (FL), a privacy-oriented distributed ML paradigm, is being gaining great interest in Internet of Things because of its capability to protect participants data privacy. Studies have been conducted to address challenges existing in standard FL, including communication efficiency and privacy-preserving. But they cannot achieve the goal of making a tradeoff between communication efficiency and model accuracy while guaranteeing privacy. This paper proposes a Conditional Random Sampling (CRS) method and implements it into the standard FL settings (CRS-FL) to tackle the above-mentioned challenges. CRS explores a stochastic coefficient based on Poisson sampling to achieve a higher probability of obtaining zero-gradient unbiasedly, and then decreases the communication overhead effectively without model accuracy degradation. Moreover, we dig out the relaxation Local Differential Privacy (LDP) guarantee conditions of CRS theoretically. Extensive experiment results indicate that (1) in communication efficiency, CRS-FL performs better than the existing methods in metric accuracy per transmission byte without model accuracy reduction in more than 7% sampling ratio (# sampling size / # model size); (2) in privacy-preserving, CRS-FL achieves no accuracy reduction compared with LDP baselines while holding the efficiency, even exceeding them in model accuracy under more sampling ratio conditions.

cs.CR↗

arXiv2023

Multi-stage Progressive Reasoning for Dunhuang Murals Inpainting

Dunhuang murals suffer from fading, breakage, surface brittleness and extensive peeling affected by prolonged environmental erosion. Image inpainting techniques are widely used in the field of digital mural inpainting. Generally speaking, for mural inpainting tasks with large area damage, it is challenging for any image inpainting method. In this paper, we design a multi-stage progressive reasoning network (MPR-Net) containing global to local receptive fields for murals inpainting. This network is capable of recursively inferring the damage boundary and progressively tightening the regional texture constraints. Moreover, to adaptively fuse plentiful information at various scales of murals, a multi-scale feature aggregation module (MFA) is designed to empower the capability to select the significant features. The execution of the model is similar to the process of a mural restorer (i.e., inpainting the structure of the damaged mural globally first and then adding the local texture details further). Our method has been evaluated through both qualitative and quantitative experiments, and the results demonstrate that it outperforms state-of-the-art image inpainting methods.

cs.CV↗