IEEE Transactions on Computational Imaging最新文献_第10页

Polarimetric Light Transport Analysis for Specular Inter-Reflection 镜面相互反射的偏振光传输分析

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-23 DOI: 10.1109/TCI.2024.3404612

Ryota Maeda;Shinsaku Hiura

{"title":"Polarimetric Light Transport Analysis for Specular Inter-Reflection","authors":"Ryota Maeda;Shinsaku Hiura","doi":"10.1109/TCI.2024.3404612","DOIUrl":"10.1109/TCI.2024.3404612","url":null,"abstract":"Polarization is well known for its ability to decompose diffuse and specular reflections. However, the existing decomposition methods only focus on direct reflection and overlook multiple reflections, especially specular inter-reflection. In this paper, we propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature: the rotation direction of linear polarization. This rotation direction serves as a discriminative factor between direct and inter-reflection on specular surfaces. To decompose the reflectance components, we actively rotate the linear polarization of incident light and analyze the rotation direction of the reflected light. We evaluate our method using both synthetic and real data, demonstrating its effectiveness in decomposing specular inter-reflections of metal objects. Furthermore, we demonstrate that our method can be combined with other decomposition methods for a detailed analysis of light transport. As a practical application, we show its effectiveness in improving the accuracy of 3D measurement against strong specular inter-reflection.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"876-887"},"PeriodicalIF":5.4,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-Based Approaches for Reconstructions With Inexact Operators in nanoCT Applications 纳米 CT 应用中基于学习的非精确算子重构方法

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-21 DOI: 10.1109/TCI.2024.3380319

Tom Lütjen;Fabian Schönfeld;Alice Oberacker;Johannes Leuschner;Maximilian Schmidt;Anne Wald;Tobias Kluth

引用次数: 0

Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part I: A Matrix-Completion Framework 基于局部-反向-一的快照光谱图像联合解混和去马赛克方法。第一部分：矩阵补全框架

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-20 DOI: 10.1109/TCI.2024.3402322

Kinan Abbas;Matthieu Puigt;Gilles Delmaire;Gilles Roussel

{"title":"Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part I: A Matrix-Completion Framework","authors":"Kinan Abbas;Matthieu Puigt;Gilles Delmaire;Gilles Roussel","doi":"10.1109/TCI.2024.3402322","DOIUrl":"10.1109/TCI.2024.3402322","url":null,"abstract":"With the recent advancements in design and processing speed, a new snapshot mosaic imaging sensor architecture (SSI) has been successfully developed, holding the potential to transform the way dynamic scenes are captured using miniaturized platforms. However, SSI systems encounter a core trade-off concerning spatial and spectral resolution due to the assignment of individual spectral bands to each pixel. While the SSI camera manufacturer provides a pipeline to process such data, we propose in this paper to process the RAW SSI data directly. We show this strategy to be much more accurate than post-processing after the pipeline. In particular, in the first part of this paper, we propose a low-rank matrix factorization and completion framework which jointly tackles both the demosaicing and the unmixing steps of the SSI data. In addition to a “natural” technique, we expand the well-known pure pixel assumption to the SSI sensor level and propose two dedicated methods to extract the endmembers. The first one can be seen as a weighted Sparse Component Analysis (SCA) method, while the second one relaxes the abundance sparsity assumption of the former. The abundances are then recovered by applying the naive approach with the fixed extracted endmembers. Finally, we experimentally validate the merits of the proposed methods using synthetically generated data and real images obtained with an SSI camera.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"848-862"},"PeriodicalIF":5.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153647","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

Channel-Spatial Attention Guided CycleGAN for CBCT-Based Synthetic CT Generation to Enable Adaptive Radiotherapy 通道空间注意力引导的 CycleGAN 用于基于 CBCT 的合成 CT 生成，以实现自适应放疗

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-20 DOI: 10.1109/TCI.2024.3402372

Yangchuan Liu;Shimin Liao;Yechen Zhu;Fuxing Deng;Zijian Zhang;Xin Gao;Tingting Cheng

{"title":"Channel-Spatial Attention Guided CycleGAN for CBCT-Based Synthetic CT Generation to Enable Adaptive Radiotherapy","authors":"Yangchuan Liu;Shimin Liao;Yechen Zhu;Fuxing Deng;Zijian Zhang;Xin Gao;Tingting Cheng","doi":"10.1109/TCI.2024.3402372","DOIUrl":"10.1109/TCI.2024.3402372","url":null,"abstract":"Cone-beam computed tomography (CBCT) is the most commonly used 3D imaging modality in image-guided radiotherapy. However, severe artifacts and inaccurate Hounsfield units render CBCT images directly unusable for dose calculations in radiotherapy planning. The deformed pCT (dpCT) image produced by aligning the planning CT (pCT) image with the CBCT image can be viewed as the corrected CBCT image. However, when the interval between pCT and CBCT scans is long, the alignment error increases, which reduces the accuracy of dose calculations based on dpCT images. This study introduces a channel-spatial attention-guided cycle-consistent generative adversarial network (cycleGAN) called TranSE-cycleGAN, which learns mapping from CBCT to dpCT images and generates synthetic CT (sCT) images similar to dpCT images to achieve CBCT image correction. To enhance the network's ability to extract global features that reflect the overall noise and artifact distribution of the image, a TranSE branch, which is composed of a SELayer and an improved window-based transformer, was added parallel to the original residual convolution branch to the cycleGAN generator. To evaluate the proposed network, we collected data from 51 patients with head-and-neck cancer who underwent both pCT and CBCT scans. Among these, 45 were used for network training, and 6 were used for network testing. The results of the comparison experiments with cycleGAN and respath-cycleGAN demonstrate that the proposed TranSE-cycleGAN excels not only in image quality evaluation metrics, including mean absolute error, root mean square error, peak signal-to-noise ratio, and structural similarity but also in the Gamma index pass rate, a metric for dose accuracy evaluation. The superiority of the proposed method indicates its potential value in adaptive radiotherapy.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"818-831"},"PeriodicalIF":5.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153545","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

RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging RED-PSM：通过对用于动态成像的因子化低秩模型去噪进行正规化

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-20 DOI: 10.1109/TCI.2024.3402347

Berk Iskender;Marc L. Klasky;Yoram Bresler

{"title":"RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging","authors":"Berk Iskender;Marc L. Klasky;Yoram Bresler","doi":"10.1109/TCI.2024.3402347","DOIUrl":"10.1109/TCI.2024.3402347","url":null,"abstract":"Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed. We propose an approach, RED-PSM, which combines for the first time two powerful techniques to address this challenging imaging problem. The first, are non-parametric factorized low rank models, also known as partially separable models (PSMs), which have been used to efficiently introduce a low-rank prior for the spatio-temporal object. The second is the recent \u0000<italic>Regularization by Denoising (RED)</i>\u0000, which provides a flexible framework to exploit the impressive performance of state-of-the-art image denoising algorithms, for various inverse problems. We propose a partially separable objective with RED and a computationally efficient and scalable optimization scheme with variable splitting and ADMM. Theoretical analysis proves the convergence of our objective to a value corresponding to a stationary point satisfying the first-order optimality conditions. Convergence is accelerated by a particular projection-domain-based initialization. We demonstrate the performance and computational improvements of our proposed RED-PSM with a learned image denoiser by comparing it to a recent deep-prior-based method known as TD-DIP. Although the main focus is on dynamic tomography, we also show performance advantages of RED-PSM in a cardiac dynamic MRI setting.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"832-847"},"PeriodicalIF":5.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part II: A Filtering-Based Framework 基于局部-反向-一的快照光谱图像联合解混和去马赛克方法。第二部分：基于滤波的框架

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-20 DOI: 10.1109/TCI.2024.3402441

Kinan Abbas;Matthieu Puigt;Gilles Delmaire;Gilles Roussel

引用次数: 0

LL-UNet++:UNet++ Based Nested Skip Connections Network for Low-Light Image Enhancement LL-UNet++：基于 UNet++ 的嵌套跳转连接网络，用于弱光图像增强

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-18 DOI: 10.1109/TCI.2024.3378091

Pengfei Shi;Xiwang Xu;Xinnan Fan;Xudong Yang;Yuanxue Xin

{"title":"LL-UNet++:UNet++ Based Nested Skip Connections Network for Low-Light Image Enhancement","authors":"Pengfei Shi;Xiwang Xu;Xinnan Fan;Xudong Yang;Yuanxue Xin","doi":"10.1109/TCI.2024.3378091","DOIUrl":"10.1109/TCI.2024.3378091","url":null,"abstract":"Enhancing low-light images presents several challenges, such as image darkness, severe color distortion, and noise. To address these issues, we propose a novel low-light image enhancement algorithm with nested skip connections based on UNet++. This design facilitates the propagation of finer features and improves information transmission, resulting in better enhancement of image brightness, reduction of color distortion, and retention of finer details. To eliminate noise potentially introduced by skip connections, we designed a specific residual block based on Instance Normalization (IN). IN can process each sample independently, allowing the model to better adapt to each image's specific lighting conditions and noise levels. In addition, we propose a new hybrid loss function that simultaneously emphasizes multiple critical attributes of an image, yielding superior enhancement results on multiple key metrics. The proposed algorithm achieves advanced performance on the LOL dataset, scoring 23.0047 and 0.8682 on the PSNR and SSIM metrics, respectively. Extensive experiments demonstrate the effectiveness and superiority of our proposed algorithm.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"510-521"},"PeriodicalIF":5.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140170982","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 Box-Spline Framework for Inverse Problems With Continuous-Domain Sparsity Constraints 针对具有连续域稀疏性约束的逆问题的箱形样条线框架

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-17 DOI: 10.1109/TCI.2024.3402376

Mehrsa Pourya;Aleix Boquet-Pujadas;Michael Unser

引用次数: 0

Deep Network Cascade for Dynamic Cardiac MRI Reconstruction With Motion Feature Incorporation and the Fourier Neural Attention 利用运动特征整合和傅立叶神经注意的动态心脏 MRI 重构深度网络级联

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-17 DOI: 10.1109/TCI.2024.3402335

Jingshuai Liu;Chen Qin;Mehrdad Yaghoobi

{"title":"Deep Network Cascade for Dynamic Cardiac MRI Reconstruction With Motion Feature Incorporation and the Fourier Neural Attention","authors":"Jingshuai Liu;Chen Qin;Mehrdad Yaghoobi","doi":"10.1109/TCI.2024.3402335","DOIUrl":"10.1109/TCI.2024.3402335","url":null,"abstract":"Magnetic resonance imaging (MRI) provides a radiation-free and non-invasive tool for clinical diagnosis. However, it suffers from a prohibitively long acquisition process for many applications. Compressed sensing (CS) methods have been used for reconstruction from under-sampled data in accelerated acquisitions. Although effective in practice, the image quality can be limited by the expressiveness of handcrafted signal priors such as sparsity. Dynamic MRI requires high spatial and temporal resolution, which makes CS to be more difficult to recover the data taken within a short scanning time. In this paper, we explore to solve the challenging inverse problem by introducing an optimization-inspired deep leaning framework to recover dynamic MRI images. A novel mask-guided motion feature incorporation (Mask-MFI) scheme is proposed to benefit the recovery of the dynamic content, and a spatio-temporal Fourier neural block (ST-FNB) is designed to improve the reconstruction performance by leveraging the redundancies in spatial and temporal domains in a computation and parameter efficient manner. The comparative experiments demonstrate that the proposed framework outperforms other state-of-the-art methods at a range of accelerations both qualitatively and quantitatively. Ablation studies confirm the effectiveness of model components. Moreover, the adaptability and generalization capacity of the introduced method are also validated, which demonstrates the potential of the application of our proposed approach to other reconstruction models to boost their performance.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"774-789"},"PeriodicalIF":5.4,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063753","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

Depth Estimation From a Single Optical Encoded Image Using a Learned Colored-Coded Aperture 使用学习型彩色编码光圈从单个光学编码图像中估算深度

IF 5.4 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-03-15 DOI: 10.1109/TCI.2024.3396700

Jhon Lopez;Edwin Vargas;Henry Arguello

{"title":"Depth Estimation From a Single Optical Encoded Image Using a Learned Colored-Coded Aperture","authors":"Jhon Lopez;Edwin Vargas;Henry Arguello","doi":"10.1109/TCI.2024.3396700","DOIUrl":"10.1109/TCI.2024.3396700","url":null,"abstract":"Depth estimation from a single image of a conventional camera is challenging since depth cues are lost during the acquisition process. State-of-the-art approaches improve the discrimination between different depths by introducing a binary-coded aperture (CA) in the lens aperture that generates different coded blur patterns at different depths. Color-coded apertures (CCA) can also produce color misalignment in the captured image, which can be utilized to estimate disparity. Leveraging advances in deep learning, more recent works have explored the data-driven design of a diffractive optical element (DOE) for encoding depth information through chromatic aberrations. However, compared with binary CA or CCA, DOEs are more expensive to fabricate and require high-precision devices. Different from previous CCA-based approaches that employ few basic colors, in this work, we propose a CCA with a greater number of color filters and richer spectral information to optically encode relevant depth information in a single snapshot. Furthermore, we propose to jointly learn the color-coded aperture (CCA) pattern and a convolutional neural network (CNN) to retrieve depth information using an end-to-end optimization approach. We demonstrate through different experiments on three different data sets that the designed color-encoding has the potential to remove depth ambiguities and provides better depth estimates compared to state-of-the-art approaches. Additionally, we build a low-cost prototype of our CCA using a photographic film and validate the proposed approach in real scenarios.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"752-761"},"PeriodicalIF":5.4,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063705","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