IEEE Transactions on Computational Imaging最新文献_第2页

Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring 尺度等变成像：图像超分辨率和去模糊的自监督学习

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-02-02 DOI: 10.1109/TCI.2026.3660011

Jérémy Scanvic;Mike Davies;Patrice Abry;Julián Tachella

{"title":"Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring","authors":"Jérémy Scanvic;Mike Davies;Patrice Abry;Julián Tachella","doi":"10.1109/TCI.2026.3660011","DOIUrl":"https://doi.org/10.1109/TCI.2026.3660011","url":null,"abstract":"Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone. However, existing approaches fail to obtain competitive performances in the problems of image super-resolution and deblurring, which play a key role in most imaging systems. In this work, we show that invariance to roto-translations is insufficient to learn from measurements that only contain low-frequency information. Instead, we propose scale-equivariant imaging, a new self-supervised approach that leverages the fact that many image distributions are approximately scale-invariant, enabling the recovery of high-frequency information lost in the measurement process. We demonstrate throughout a series of experiments on real datasets that the proposed method outperforms other self-supervised approaches, and obtains performances on par with fully supervised learning.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"480-490"},"PeriodicalIF":4.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reference-Free Enhancement of Forward-Looking Sonar Images: Bridging Cross-Modal Degradation Gaps Through Deformable Wavelet Scattering Transform and Multi-Frame Fusion 前视声纳图像的无参考增强：通过可变形小波散射变换和多帧融合弥合跨模态退化间隙

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-29 DOI: 10.1109/TCI.2026.3658954

Zhisheng Zhang;Peng Zhang;Fengxiang Wang;Liangli Ma;Fuchun Sun

{"title":"Reference-Free Enhancement of Forward-Looking Sonar Images: Bridging Cross-Modal Degradation Gaps Through Deformable Wavelet Scattering Transform and Multi-Frame Fusion","authors":"Zhisheng Zhang;Peng Zhang;Fengxiang Wang;Liangli Ma;Fuchun Sun","doi":"10.1109/TCI.2026.3658954","DOIUrl":"https://doi.org/10.1109/TCI.2026.3658954","url":null,"abstract":"Enhancing forward-looking sonar (FLS) imagery is crucial for underwater target detection. Existing deep learning approaches typically rely on supervised training with simulated data, but their generalization is constrained by the scarcity of real-world paired datasets.Recent reference-free methods can denoise single frames, yet their reliance on local statistics makes them fragile at low signal-to-noise ratios (SNRs) and prone to losing global structural consistency. Multispectral/hyperspectral remote-sensing (RS) and FLS imagery share speckle-like textures and range-dependent contrast, so large-scale <italic>supervised</i> RS priors are a natural candidate for transfer; however, the cross-modal degradation gap still causes naïve feature transfer to over-smooth targets and compress brightness. To exploit these priors safely, we follow a cross-modal <italic>align-before-use</i> strategy based on a Deformable Wavelet Scattering Transform (DWST) Feature Bridge. The bridge learns small, regularized scale–orientation offsets and maps sonar images into a stable, translation-invariant feature space aligned with supervised high-/low-resolution RS models. On top of this space, a HighRes-Net-style multi-frame fusion backbone is fine-tuned end-to-end in a fully reference-free manner on noisy sonar sequences, leveraging inter-frame complementarity for speckle suppression and contrast/resolution gains while preserving key sonar imaging characteristics. Experiments on an in-house C900-II pool dataset, the public DEBRIS forward-looking sonar benchmark, and the UATD underwater acoustic target detection dataset show that the proposed DWST-augmented fusion frameworksubstantially outperforms existing reference-free methods in noise suppression, edge preservation, and brightness enhancement, and consistently improves downstream object detection, underscoring its practical effectiveness for underwater applications.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"470-479"},"PeriodicalIF":4.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal Maps for Dynamic MRI Reconstruction 动态MRI重建的时空图

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-26 DOI: 10.1109/TCI.2026.3657289

Rodrigo A. Lobos;Xiaokai Wang;Rex T. L. Fung;Yongli He;David Frey;Dinank Gupta;Zhongming Liu;Jeffrey A. Fessler;Douglas C. Noll

{"title":"Spatiotemporal Maps for Dynamic MRI Reconstruction","authors":"Rodrigo A. Lobos;Xiaokai Wang;Rex T. L. Fung;Yongli He;David Frey;Dinank Gupta;Zhongming Liu;Jeffrey A. Fessler;Douglas C. Noll","doi":"10.1109/TCI.2026.3657289","DOIUrl":"https://doi.org/10.1109/TCI.2026.3657289","url":null,"abstract":"The partially separable functions (PSF) model is commonly adopted in dynamic MRI reconstruction, as is the underlying signal model in many reconstruction methods including the ones relying on low-rank assumptions. Even though the PSF model offers a parsimonious representation of the dynamic MRI signal in several applications, its representation capabilities tend to decrease in scenarios where voxels present different temporal/spectral characteristics at different spatial locations. In this work we account for this limitation by proposing a new model, called spatiotemporal maps (STMs), that leverages autoregressive properties of (k, t)-space. The STM model decomposes the spatiotemporal MRI signal into a sum of components, each one consisting of a product between a spatial function and a temporal function that depends on the spatial location. The proposed model can be interpreted as an extension of the PSF model whose temporal functions are independent of the spatial location. We show that spatiotemporal maps can be efficiently computed from autocalibration data by using advanced signal processing and randomized linear algebra techniques, enabling STMs to be used as part of many reconstruction frameworks for accelerated dynamic MRI. As proof-of-concept illustrations, we show that STMs can be used to reconstruct both 2D single-channel animal gastrointestinal MRI data and 3D multichannel human functional MRI data.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"416-430"},"PeriodicalIF":4.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Breaking the Resolution Barrier in Microscopic Imaging: An Optics-Preserving Super-Resolution System Combining Motion Modulation and Deep Learning 突破显微成像的分辨率障碍：结合运动调制和深度学习的保光超分辨率系统

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-23 DOI: 10.1109/TCI.2026.3657473

Xinjie Bi;Shuo Kong;Guoliang Lu;Peng Yan

{"title":"Breaking the Resolution Barrier in Microscopic Imaging: An Optics-Preserving Super-Resolution System Combining Motion Modulation and Deep Learning","authors":"Xinjie Bi;Shuo Kong;Guoliang Lu;Peng Yan","doi":"10.1109/TCI.2026.3657473","DOIUrl":"https://doi.org/10.1109/TCI.2026.3657473","url":null,"abstract":"With the rapid development of micro/nano technology, the requirements for high-resolution images have become increasingly stringent. However, conventional microscopic imaging suffers from information degradation and irreversible sub-pixel detail loss, which fundamentally limits image quality improvement. To address this issue, this study designs a super-resolution reconstruction system based on precision motion modulation and proposes a generative adversarial network (GAN) reconstruction algorithm incorporating variable sequence fusion and attention mechanisms to achieve super-resolution imaging. The proposed system implements a novel computational microscopy framework employing multi-spatial-view sampling, where a sliding-window-inspired acquisition strategy enables the systematic recovery of sub-pixel-scale image information that would otherwise be lost in conventional imaging. Subsequently, the GAN-based algorithm reconstructs high-resolution images. Experimental results demonstrate that the system effectively extends the sub-pixel information of the original image without modifying the existing optical-physical system. It enhances the model’s reconstruction capability in terms of texture details and sharpness, significantly improving the overall quality of microscopic reconstructed images, and highlights the advantage of the algorithm in measurement accuracy.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"364-377"},"PeriodicalIF":4.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Compressive Sensing Imaging for Quantization Bit Erasure 量化位擦除的鲁棒压缩感知成像

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-20 DOI: 10.1109/TCI.2026.3656014

Zan Chen;Chuyuan Chen;Bo Wang;Junhao Zhu;Yuanjing Feng;Yongqiang Li;Xingsong Hou;Xueming Qian

{"title":"Robust Compressive Sensing Imaging for Quantization Bit Erasure","authors":"Zan Chen;Chuyuan Chen;Bo Wang;Junhao Zhu;Yuanjing Feng;Yongqiang Li;Xingsong Hou;Xueming Qian","doi":"10.1109/TCI.2026.3656014","DOIUrl":"https://doi.org/10.1109/TCI.2026.3656014","url":null,"abstract":"Existing compressive sensing (CS) reconstruction algorithms are primarily designed for deterministic measurements. However, in real-world scenarios, quantization bit erasures during transmission or storage introduce uncertainty into measurements and significantly complicate reconstruction. We extend the CS framework to explicitly handle such bit-level uncertainty, enabling robust image recovery from quantized measurements with missing bits. We begin by enumerating all valid combinations of the erased quantization bits to construct a candidate set of feasible measurement values. This candidate set is then incorporated as a constraint in a newly formulated inverse problem. We propose an iterative plug-and-play algorithm to solve this problem, alternating between two key steps: (1) an image update using a pretrained denoiser, and (2) a measurement update via a soft-min projection strategy accelerated by coordinate descent. Extensive experiments demonstrate the effectiveness of the proposed approach, achieving high-quality reconstructions even under extremely low sampling rates and severe erasure conditions. Our framework offers a scalable and principled solution for bit-erasure-robust CS reconstruction in error-prone and resource-constrained imaging environments.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"431-444"},"PeriodicalIF":4.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IE-GADCI: An End-to-End Incoherence-Enhanced Generative Adversarial Deep Compressive Imaging IE-GADCI：端到端非相干增强生成对抗深度压缩成像

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-20 DOI: 10.1109/TCI.2026.3656024

Kangning Zhang;Yifei Sun;Varun Yelluru;Weijian Yang

{"title":"IE-GADCI: An End-to-End Incoherence-Enhanced Generative Adversarial Deep Compressive Imaging","authors":"Kangning Zhang;Yifei Sun;Varun Yelluru;Weijian Yang","doi":"10.1109/TCI.2026.3656024","DOIUrl":"https://doi.org/10.1109/TCI.2026.3656024","url":null,"abstract":"Single-pixel imaging (SPI) within the framework of compressive sensing (CS) is a powerful technique that enables image acquisition at sub-Nyquist sampling rates by leveraging the sparse latent representations of the object scenes. As a cost-effective alternative to focal plane array cameras, SPI has been explored for various imaging applications. We recently introduced a novel block-scanning SPI approach that samples the scene using a single, learnable illumination pattern, which substantially enhanced acquisition speed compared to traditional SPI systems that rely on pattern switching via digital micromirror devices. In this work, we present a new computational framework, termed Incoherence-Enhanced Generative Adversarial Deep Compressive Imaging (IE-GADCI), designed to jointly optimize both the illumination pattern and the image reconstruction algorithm for block-scanning SPI, under the principle of compressive sensing. Our architecture employs a neural network that learns the latent sparse representations of the scene and integrates information from both the image and sparsity domains to achieve high-resolution reconstructions with high computational efficiency. A key innovation of IE-GADCI is its optimization of the incoherence between the illumination pattern and the sparse representation, which substantially improves reconstruction fidelity. We validated the performance of IE-GADCI through numerical simulations on natural and biomedical image datasets. We benchmarked IE-GADCI against state-of-the-art methods used in SPI, and additionally, single-image super-resolution (SISR), given the conceptual similarity between block-scanning SPI and SISR. At a subsampling rate of just 1.5625%, IE-GADCI achieves a peak signal-to-noise ratio (PSNR) exceeding that of competing methods by more than 2 dB. These results highlight the potential of IE-GADCI for high-speed, high-fidelity imaging in applications such as consumer electronics and biomedical imaging, including calcium imaging for neuronal activity recording.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"391-405"},"PeriodicalIF":4.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction With Gradient-Driven Denoisers 梯度降噪压缩感知MRI重构的收敛广义Krylov子空间方法

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-19 DOI: 10.1109/TCI.2026.3655489

Tao Hong;Umberto Villa;Jeffrey A. Fessler

{"title":"A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction With Gradient-Driven Denoisers","authors":"Tao Hong;Umberto Villa;Jeffrey A. Fessler","doi":"10.1109/TCI.2026.3655489","DOIUrl":"https://doi.org/10.1109/TCI.2026.3655489","url":null,"abstract":"Model-based reconstruction plays a key role in compressed sensing (CS) MRI, as it incorporates effective image regularizers to improve the quality of reconstruction. The Plug-and-Play and Regularization-by-Denoising frameworks leverage advanced denoisers (e.g., convolutional neural network (CNN)-based denoisers) and have demonstrated strong empirical performance. However, their theoretical guarantees remain limited, as practical CNNs often violate key assumptions. In contrast, gradient-driven denoisers achieve competitive performance, and the required assumptions for theoretical analysis are easily satisfied. However, solving the associated optimization problem remains computationally demanding. To address this challenge, we propose a generalized Krylov subspace method (GKSM) to solve the optimization problem efficiently. Moreover, we also establish rigorous convergence guarantees for GKSM in nonconvex settings. Numerical experiments on CS MRI reconstruction with spiral and radial acquisitions validate both the computational efficiency of GKSM and the accuracy of the theoretical predictions. The proposed optimization method is applicable to any linear inverse problem.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"378-390"},"PeriodicalIF":4.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Translational Motion Compensation of ISAR Imaging for Uniformly Accelerated Motion Targets Based on MPD-TSLS Under Low SNR 低信噪比下基于MPD-TSLS的均匀加速运动目标ISAR成像关节平移运动补偿

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-19 DOI: 10.1109/TCI.2026.3655460

Tao Liu;Yu Wang;Biao Tian;Shiyou Xu;Zengping Chen

{"title":"Joint Translational Motion Compensation of ISAR Imaging for Uniformly Accelerated Motion Targets Based on MPD-TSLS Under Low SNR","authors":"Tao Liu;Yu Wang;Biao Tian;Shiyou Xu;Zengping Chen","doi":"10.1109/TCI.2026.3655460","DOIUrl":"https://doi.org/10.1109/TCI.2026.3655460","url":null,"abstract":"Effective and accurate translational motion compensation is crucial for inverse synthetic aperture radar (ISAR) imaging. Traditional data-based translational motion compensation methods are inapplicable to uniformly accelerated targets in low signal-to-noise ratio (SNR) scenarios. In this paper, a parametric approach is proposed based on joint modified phase difference and two-step least squares (MPD-TSLS). The method employs a second-order polynomial model for translational motion, whereby the energy of all scatterers is converted to a single range and Doppler cell through MPD operation, respectively. The estimated acceleration and velocity are then obtained by TSLS. To enhance precision, an optimization approach based on LS for refining polynomial parameters is also employed. The proposed method achieves a substantial increase in SNR, ensuring precise compensation accuracy while maintaining high computational efficiency by relying exclusively on fast Fourier transform (FFT) and matrix operations. The experimental results obtained from both simulated and real datasets fully verify that the proposed method exhibits superior performance compared with the other implemented methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"349-363"},"PeriodicalIF":4.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Lightweight Unrolled Network for High Dynamic Range Modulo Imaging 用于高动态范围模成像的深度轻量级展开网络

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-19 DOI: 10.1109/TCI.2026.3655494

Brayan Monroy;Jorge Bacca

引用次数: 0

DPD-DEMD: Denoising Prior Guided Weakly Supervised Image Domain Dual-Energy Material Decomposition 去噪先验引导弱监督图像域双能材料分解

IF 4.8 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2026-01-16 DOI: 10.1109/TCI.2026.3654807

Xinyun Zhong;Xu Zhuo;Tianling Lyu;Yikun Zhang;Qianjin Feng;Guotao Quan;Xu Ji;Yang Chen

{"title":"DPD-DEMD: Denoising Prior Guided Weakly Supervised Image Domain Dual-Energy Material Decomposition","authors":"Xinyun Zhong;Xu Zhuo;Tianling Lyu;Yikun Zhang;Qianjin Feng;Guotao Quan;Xu Ji;Yang Chen","doi":"10.1109/TCI.2026.3654807","DOIUrl":"https://doi.org/10.1109/TCI.2026.3654807","url":null,"abstract":"Dual-energy material decomposition is widely used in clinical diagnosis, especially for material characterization. However, conventional image-domain methods suffer from noise amplification, thus reducing signal-to-noise ratio and compromising diagnostic accuracy. Although deep learning approaches have shown significant progress, they often require high-quality paired or unpaired labels, limiting their clinical application. To address these issues, this work explores the feasibility of weakly supervised methods and proposes a denoising prior guided weakly supervised learning framework, DPD-DEMD, to achieve high-accuracy image-domain dual-energy material decomposition. DPD-DEMD utilizes pretrained CT denoising models to construct robust priors for our dual energy material decomposition task. Furthermore, we propose an adaptive confidence mask mechanism for pseudo label generation and a multi-prior fusion strategy, thereby substantially improving the stability and reliability of the weakly supervised learning process. In addition, we fully exploit the correlation between dual energy images and further propose global-local regularization loss to improve the material decomposition accuracy. Extensive experiments conducted on both simulated and clinical datasets verify the superior performance and robustness of the proposed method, thereby demonstrating its potential clinical value in material decomposition.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"12 ","pages":"334-348"},"PeriodicalIF":4.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0