IEEE Transactions on Computational Imaging最新文献_第9页

Enhancing MRF Reconstruction: A Model-Based Deep Learning Approach Leveraging Learned Sparsity and Physics Priors 增强 MRF 重构：利用学习稀疏性和物理先验的基于模型的深度学习方法

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-08-07 DOI: 10.1109/TCI.2024.3440008

Peng Li;Yue Hu

{"title":"Enhancing MRF Reconstruction: A Model-Based Deep Learning Approach Leveraging Learned Sparsity and Physics Priors","authors":"Peng Li;Yue Hu","doi":"10.1109/TCI.2024.3440008","DOIUrl":"10.1109/TCI.2024.3440008","url":null,"abstract":"Deep learning has shown great promise in improving the speed and accuracy of parameter map estimation in magnetic resonance fingerprinting (MRF). However, many existing methods rely on physics-free networks, leading to a staged processing strategy. This strategy involves the initial reconstruction of acquired non-Cartesian undersampled measurements, followed by subsequent parameter map estimation. Unfortunately, such a staged processing strategy may lead to partial information loss and limit the eventual accuracy of parameter imaging. To overcome these challenges, in this paper, we propose a novel model-based deep learning approach that directly reconstructs parameter maps from non-Cartesian undersampled measurements. Specifically, our approach first incorporates MRF imaging physics priors and data correlation constraints into a unified reconstruction model. The proposed model-based network, named LS-MRF-Net, is then defined by unrolling the iterative procedures of the reconstruction model into a deep neural network. Notably, a learned sparsity layer is proposed to exploit the optimal transform domain for sparse representation of high-dimensional MRF data. Additionally, we incorporate a mapping layer and a Bloch response dynamic layer to seamlessly integrate the MRF imaging physics priors into the network. Experimental results on both simulated and \u0000<italic>in vivo</i>\u0000 datasets demonstrate that the proposed method can significantly reduce computational time while enhancing MRF reconstruction performance.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1221-1234"},"PeriodicalIF":4.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141941879","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

NAS Powered Deep Image Prior for Electrical Impedance Tomography NAS 驱动的电阻抗断层扫描深度图像先验

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-08-07 DOI: 10.1109/TCI.2024.3440063

Haoyuan Xia;Qianxue Shan;Junwu Wang;Dong Liu

引用次数: 0

Real-Time Model-Based Quantitative Ultrasound and Radar 基于模型的实时定量超声波和雷达

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-08-02 DOI: 10.1109/TCI.2024.3436537

Tom Sharon;Yonina C. Eldar

引用次数: 0

CMEFusion: Cross-Modal Enhancement and Fusion of FIR and Visible Images CMEFusion：傅立叶变换和可见光图像的跨模态增强与融合

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-08-02 DOI: 10.1109/TCI.2024.3436716

Xi Tong;Xing Luo;Jiangxin Yang;Yanpeng Cao

{"title":"CMEFusion: Cross-Modal Enhancement and Fusion of FIR and Visible Images","authors":"Xi Tong;Xing Luo;Jiangxin Yang;Yanpeng Cao","doi":"10.1109/TCI.2024.3436716","DOIUrl":"10.1109/TCI.2024.3436716","url":null,"abstract":"The fusion of far infrared (FIR) and visible images aims to generate a high-quality composite image that contains salient structures and abundant texture details for human visual perception. However, the existing fusion methods typically fall short of utilizing complementary source image characteristics to boost the features extracted from degraded visible or FIR images, thus they cannot generate satisfactory fusion results in adverse lighting or weather conditions. In this paper, we propose a novel Cross-Modal multispectral image Enhancement and Fusion framework (CMEFusion), which adaptively enhances both FIR and visible inputs by leveraging complementary cross-modal features to further facilitate multispectral feature aggregation. Specifically, we first present a new cross-modal image enhancement sub-network (CMIENet), which is built on a CNN-Transformer hybrid architecture to perform the complementary exchange of local-salient and global-contextual features extracted from FIR and visible modalities, respectively. Then, we design a gradient-content differential fusion sub-network (GCDFNet) to progressively integrate decoupled gradient and content information via modified central difference convolution. Finally, we present a comprehensive joint enhancement-fusion multi-term loss function to drive the model to narrow the optimization gap between the above-mentioned two sub-networks based on the self-supervised aspects of exposure, color, structure, and intensity. In this manner, the proposed CMEFusion model facilitates better-performing visible and FIR image fusion in an end-to-end way, achieving enhanced visual quality with more natural and realistic appearances. Extensive experiments validate that CMEFusion surpasses state-of-the-art image fusion algorithms, as evidenced by superior performance in both visual quality and quantitative evaluations.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1331-1345"},"PeriodicalIF":4.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886639","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

Generative AI Enables Synthesizing Cross-Modality Brain Image via Multi-Level-Latent Representation Learning 生成式人工智能通过多vel-Latent表征学习实现跨模态脑图像合成

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-29 DOI: 10.1109/TCI.2024.3434724

Senrong You;Bin Yuan;Zhihan Lyu;Charles K. Chui;C. L. Philip Chen;Baiying Lei;Shuqiang Wang

{"title":"Generative AI Enables Synthesizing Cross-Modality Brain Image via Multi-Level-Latent Representation Learning","authors":"Senrong You;Bin Yuan;Zhihan Lyu;Charles K. Chui;C. L. Philip Chen;Baiying Lei;Shuqiang Wang","doi":"10.1109/TCI.2024.3434724","DOIUrl":"10.1109/TCI.2024.3434724","url":null,"abstract":"Multiple brain imaging modalities can provide complementary pathologic information for clinical diagnosis. However, it is huge challenge to acquire enough modalities in clinical practice. In this work, a cross-modality reconstruction model, called fine-grain aware generative adversarial network (FA-GAN), is proposed to reconstruct the target modality images of brain from the 2D source modality images with a dual-stages manner. The FA-GAN is able to mine the multi-level shared latent representations from the source modality images and then reconstruct the target modality image from coarse to fine progressively. Specifically, in the coarse stage, the Multi-Grain Extractor firstly extracts and disentangles the shared latent features from the source modality images, and synthesizes the coarse target modality images with a pyramidal network. The Feature-Joint Encoder then encodes the latent features and frequency features jointly. In the fine stage, the Fine-Texture Generator is fed with the joint codes to fine tune the reconstruction of the fine-grained target modality. The wavelet transformation module is employed to extract the frequency codes and guide the Fine-Texture Generator to synthesize finer textures. Comprehensive experiments from MR to PET images on ADNI datasets demonstrate that the proposed model achieves finer structure recovery and outperforms the competing methods quantitatively and qualitatively.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1152-1164"},"PeriodicalIF":4.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869570","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

Learning to Describe Scenes via Privacy-Aware Designed Optical Lens 通过隐私意识设计的光学透镜学习描述场景

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-29 DOI: 10.1109/TCI.2024.3426975

Paula Arguello;Jhon Lopez;Karen Sanchez;Carlos Hinojosa;Fernando Rojas-Morales;Henry Arguello

{"title":"Learning to Describe Scenes via Privacy-Aware Designed Optical Lens","authors":"Paula Arguello;Jhon Lopez;Karen Sanchez;Carlos Hinojosa;Fernando Rojas-Morales;Henry Arguello","doi":"10.1109/TCI.2024.3426975","DOIUrl":"10.1109/TCI.2024.3426975","url":null,"abstract":"Scene captioning consists of accurately describing the visual information using text, leveraging the capabilities of computer vision and natural language processing. However, current image captioning methods are trained on high-resolution images that may contain private information about individuals within the scene, such as facial attributes or sensitive data. This raises concerns about whether machines require high-resolution images and how we can protect the private information of the users. In this work, we aim to protect privacy in the scene captioning task by addressing the issue directly from the optics before image acquisition. Specifically, motivated by the emerging trend of integrating optics design with algorithms, we introduce a learned refractive lens into the camera to ensure privacy. Our optimized lens obscures sensitive visual attributes, such as faces, ethnicity, gender, and more, in the acquired image while extracting relevant features, enabling descriptions even from highly distorted images. By optimizing the refractive lens and a deep network architecture for image captioning end-to-end, we achieve description generation directly from our distorted images. We validate our approach with extensive simulations and hardware experiments. Our results show that we achieve a better trade-off between privacy and utility when compared to conventional non-privacy-preserving methods on the COCO dataset. For instance, our approach successfully conceals private information within the scene while achieving a BLEU-4 score of 27.0 on the COCO test set.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1069-1079"},"PeriodicalIF":4.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869573","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

Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics 利用可变潜动力对时空流进行单像素成像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-29 DOI: 10.1109/TCI.2024.3434541

Aleksei Sholokhov;Saleh Nabi;Joshua Rapp;Steven L. Brunton;J. Nathan Kutz;Petros T. Boufounos;Hassan Mansour

{"title":"Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics","authors":"Aleksei Sholokhov;Saleh Nabi;Joshua Rapp;Steven L. Brunton;J. Nathan Kutz;Petros T. Boufounos;Hassan Mansour","doi":"10.1109/TCI.2024.3434541","DOIUrl":"10.1109/TCI.2024.3434541","url":null,"abstract":"Imaging dynamic spatio-temporal flows typically requires high-speed, high-resolution sensors that may be physically or economically prohibitive. Single-pixel imaging (SPI) has emerged as a low-cost acquisition technique where light from a scene is projected through a spatial light modulator onto a single photodiode with a high temporal acquisition rate. The scene is then reconstructed from the temporal samples using computational techniques that leverage prior assumptions on the scene structure. In this paper, we propose to image spatio-temporal flows from incomplete measurements by leveraging scene priors in the form of a reduced-order model (ROM) of the dynamics learned from training data examples. By combining SPI acquisition with the ROM prior implemented as a neural ordinary differential equation, we achieve high-quality image sequence reconstruction with significantly reduced data requirements. Specifically, our approach achieves similar performance levels to leading methods despite using one to two orders of magnitude fewer samples. We demonstrate superior reconstruction at low sampling rates for simulated trajectories governed by Burgers' equation, Kolmogorov flow, and turbulent plumes emulating gas leaks.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1124-1138"},"PeriodicalIF":4.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869571","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

Region-Based Spectral-Spatial Mutual Induction Network for Hyperspectral Image Reconstruction 基于区域的光谱空间互感网络用于高光谱图像重建

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-18 DOI: 10.1109/TCI.2024.3430478

Jianan Li;Wangcai Zhao;Tingfa Xu

{"title":"Region-Based Spectral-Spatial Mutual Induction Network for Hyperspectral Image Reconstruction","authors":"Jianan Li;Wangcai Zhao;Tingfa Xu","doi":"10.1109/TCI.2024.3430478","DOIUrl":"10.1109/TCI.2024.3430478","url":null,"abstract":"In hyperspectral compression imaging, the choice of a reconstruction algorithm is critical for achieving high-quality results. Hyperspectral Images (HSI) have strong spectral-spatial correlations within local regions, valuable for reconstruction. However, existing learning-based methods often overlook regional variations by treating the entire image as a whole. To address this, we propose a novel region-based iterative approach for HSI reconstruction. We introduce a deep unfolding method augmented with a Region-based Spectral-Spatial Mutual Induction (RSSMI) network to model regional priors. Our approach involves partitioning the image into regions during each unfolding phase. Within each region, we employ a spatial-guided spectral attention module for holistic spectral relationships and a spectral-guided spatial attention module for spatial details. By leveraging mutual induction, our method simultaneously recovers spectral and spatial information. Furthermore, we address the issue of favoring easy-to-learn regions by introducing Focal Region Loss that dynamically adjusts loss weights for regions, emphasizing those that are harder to reconstruct. Experimental results demonstrate that our method achieves competitive performance and excels in spectrum and texture reconstruction on both simulated and real HSI datasets.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1139-1151"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740335","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

Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks 利用生成扩散网络在低剂量 X 射线 CT 中减轻病灶的超级分辨率

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-18 DOI: 10.1109/TCI.2024.3430487

Carlos M. Restrepo-Galeano;Gonzalo R. Arce

{"title":"Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks","authors":"Carlos M. Restrepo-Galeano;Gonzalo R. Arce","doi":"10.1109/TCI.2024.3430487","DOIUrl":"10.1109/TCI.2024.3430487","url":null,"abstract":"Advancing the resolution capabilities of X-ray CT imaging, particularly in low-dose applications, is a paramount pursuit in the field. This quest for superior spatial detail is hindered by the pervasive issue of focal spot blooming, which plagues medical scanners due to the finite nature of the emittance surface in X-ray sources. Such a phenomenon introduces optical distortions in the measurements that limit the achievable resolution. In response to this challenge, we introduce a novel approach: Focal Spot Diffusion CT (FSD-CT). Unlike traditional methods that rely on limited and simplified idealizations of X-ray models, FSD-CT adopts a more complex, realistic representation of X-ray sources. FSD-CT leverages a generative diffusion-based reconstruction framework, guided by a forward imaging model for sample consistency and a frequency selection module for enhanced spectral content. FSD-CT successfully mitigates focal spot blooming without imposing a significant computational burden when compared to other diffusion-based reconstruction methods, offering a versatile solution for improving CT resolution. Computational experiments using simulations based on commercial medical scanners show FSD-CT delivers gains of up to 4 dB in fan-beam tomography compared to benchmarks such as filtered backprojection, end-to-end CNNs, and state-of-the-art diffusion models. The technique's robustness is confirmed in challenging scenarios, including sparse angle CT, off-distribution samples, and reconstructions from real projections. FSD-CT helps to overcome limitations in spatial resolution and offers a plausible solution that could be applied in clinical CT imaging after more in-depth studies are conducted.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1111-1123"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740334","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

Side Information-Assisted Low-Dose CT Reconstruction 侧面信息辅助低剂量 CT 重建

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-18 DOI: 10.1109/TCI.2024.3430469

Yuanke Zhang;Rujuan Cao;Fan Xu;Rui Zhang;Fengjuan Jiang;Jing Meng;Fei Ma;Yanfei Guo;Jianlei Liu

{"title":"Side Information-Assisted Low-Dose CT Reconstruction","authors":"Yuanke Zhang;Rujuan Cao;Fan Xu;Rui Zhang;Fengjuan Jiang;Jing Meng;Fei Ma;Yanfei Guo;Jianlei Liu","doi":"10.1109/TCI.2024.3430469","DOIUrl":"10.1109/TCI.2024.3430469","url":null,"abstract":"CT images from individual patients or different patient populations typically share similar radiological features such as textures and structures. In model-based iterative reconstruction (MBIR) for low-dose CT (LDCT) imaging, image quality enhancement can be achieved not only by relying on the intrinsic raw data, but also by incorporating side information extracted from high-quality normal-dose CT (NDCT) exemplar images. The additional side information helps overcome the inherent limitations of raw data in low-dose scanning and offers potential improvements in LDCT image quality. This study investigates the effectiveness of side information-assisted MBIR (SI-MBIR) in enhancing the quality of LDCT images. Specifically, we propose to use the noise-free exemplar images to generate side information that aligns with the structural features of regions of interest (ROIs) in the target image. Each ROI is enhanced with a custom-designed prior subspace that is derived from similar exemplar samples and reflects its unique structural and textural characteristics. We then propose an adaptive sparse modeling approach, in particular, a weighted Laplace distribution model for the prior subspace. The weighted Laplace model is carefully tuned to match the signal-to-noise ratio (SNR) of each transform band, allowing adaptive sparse modeling on different bands. Furthermore, we propose an efficient CT reconstruction algorithm based on this adaptive sparse model. Using the alternating direction method of multipliers (ADMM) framework, an optimization method for this reconstruction algorithm has been formulated. Extensive experimental studies were conducted to validate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can achieve noticeable improvements over some state-of-the-art MBIR methods in terms of noise suppression and texture preservation.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1080-1093"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740336","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