IEEE Transactions on Computational Imaging最新文献_第4页

DAMMER: Direct Adaptive Multi-Resolution MEsh Reconstruction From X-Ray Measurements DAMMER：从x射线测量直接自适应多分辨率网格重建

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-07-10 DOI: 10.1109/TCI.2025.3587408

Jannes Merckx;Arnold J. den Dekker;Jan Sijbers;Jan De Beenhouwer

{"title":"DAMMER: Direct Adaptive Multi-Resolution MEsh Reconstruction From X-Ray Measurements","authors":"Jannes Merckx;Arnold J. den Dekker;Jan Sijbers;Jan De Beenhouwer","doi":"10.1109/TCI.2025.3587408","DOIUrl":"https://doi.org/10.1109/TCI.2025.3587408","url":null,"abstract":"X-ray computed tomography (XCT) reconstructs a scanned object using measured projection data, with the object typically represented on a voxel grid during the reconstruction process. However, since material interfaces typically do not align with voxel boundaries, a voxel representation inherently suffers from partial volume effects. This paper presents DAMMER: a method that reconstructs a multi-resolution triangle mesh to represent the attenuation values of a piecewise homogeneous object, often encountered in industrial CT, based on X-ray projection data of this object. DAMMER progressively reconstructs this mesh to match the object. For this, different homogeneous segments are created based on an agglomerative hierarchical clustering procedure, which targets a compact object description by optimizing a weighted sum of projection difference and number of edges between different segments. These segments are progressively optimized to match the homogeneous materials in the object. Simulation and real data experiments show that DAMMER generates significantly more accurate reconstructions compared to pixel grid reconstruction methods, outperforming conventional voxel-based methods in capturing the true geometry of complex material boundaries.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"926-941"},"PeriodicalIF":4.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671281","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

Pattern Research on Sparse Camera Array for Super-Resolution Imaging 稀疏相机阵列超分辨率成像方向图研究

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-07-10 DOI: 10.1109/TCI.2025.3588025

Tianren Li;Yu Zhang;Tengda Huang;Naishu Jia;Fulin Liu;Yufu Qu;Zhenzhong Wei

引用次数: 0

Vignetting Correction Through Color-Intensity Map Entropy Optimization 基于色强图熵优化的渐晕校正

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-26 DOI: 10.1109/TCI.2025.3583465

Zhuang He;Hai-Miao Hu;Likun Gao;Haoxin Hu;Xinhui Xue;Zhenglin Tang;Difeng Zhu;Haowen Zheng;Chongze Wang

引用次数: 0

G2L-Stereo: Global to Local Two-Stage Real-Time Stereo Matching Network G2L-Stereo：全局到局部两阶段实时立体匹配网络

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-19 DOI: 10.1109/TCI.2025.3581105

Jie Tang;Gaofeng Peng;Jialu Liu;Bo Yu

{"title":"G2L-Stereo: Global to Local Two-Stage Real-Time Stereo Matching Network","authors":"Jie Tang;Gaofeng Peng;Jialu Liu;Bo Yu","doi":"10.1109/TCI.2025.3581105","DOIUrl":"https://doi.org/10.1109/TCI.2025.3581105","url":null,"abstract":"Developing fast and accurate stereo matching algorithms is crucial for real-world embedded vision applications. Depth information plays a significant role in scene understanding, and depth calculated through stereo matching is generally considered to be more precise and reliable than that obtained from monocular depth estimation. However, speed-oriented stereo matching methods often suffer from poor feature representation due to sparse sampling and detail loss caused by unreasonable disparity allocation during upsampling. To address these issues, we propose G2L-Stereo, a two-stage real-time stereo matching network that combines global disparity range prediction and local disparity range prediction. In the global disparity range prediction stage, we introduce feature-guided connections for cost aggregation, enhancing the expressive power of sparse features by aligning the feature space across different scales of cost volumes. We also incorporate confidence estimation into the upsampling algorithm to reduce the propagation of inaccurate disparities during upsampling, yielding more precise disparity maps. In the local disparity range prediction stage, we develop a disparity refinement module guided by neighborhood similarity. This module aggregates similar neighboring costs to estimate disparity residuals and refine disparities, restoring lost details in the low-resolution disparity map and further enhancing disparity accuracy. Extensive experiments on the SceneFlow and KITTI datasets validate the effectiveness of our model, showing that G2L-Stereo achieves fast inference while maintaining accuracy comparable to state-of-the-art methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"852-863"},"PeriodicalIF":4.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524418","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

Energy-Coded Spectral CT Imaging Method Based on Projection Mix Separation 基于投影混合分离的能量编码光谱CT成像方法

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-11 DOI: 10.1109/TCI.2025.3578762

Xiaojie Zhao;Yihong Li;Yan Han;Ping Chen;Jiaotong Wei

引用次数: 0

Iterative Collaboration Network Guided by Reconstruction Prior for Medical Image Super-Resolution 基于先验重构的医学图像超分辨率迭代协同网络

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-06 DOI: 10.1109/TCI.2025.3577340

Xiaoyan Kui;Zexin Ji;Beiji Zou;Yang Li;Yulan Dai;Liming Chen;Pierre Vera;Su Ruan

{"title":"Iterative Collaboration Network Guided by Reconstruction Prior for Medical Image Super-Resolution","authors":"Xiaoyan Kui;Zexin Ji;Beiji Zou;Yang Li;Yulan Dai;Liming Chen;Pierre Vera;Su Ruan","doi":"10.1109/TCI.2025.3577340","DOIUrl":"https://doi.org/10.1109/TCI.2025.3577340","url":null,"abstract":"High-resolution medical images can provide more detailed information for better diagnosis. Conventional medical image super-resolution relies on a single task which first performs the extraction of the features and then upscaling based on the features. The features extracted may not be complete for super-resolution. Recent multi-task learning, including reconstruction and super-resolution, is a good solution to obtain additional relevant information. The interaction between the two tasks is often insufficient, which still leads to incomplete and less relevant deep features. To address above limitations, we propose an iterative collaboration network (ICONet) to improve communications between tasks by progressively incorporating reconstruction prior to the super-resolution learning procedure in an iterative collaboration way. It consists of a reconstruction branch, a super-resolution branch, and a SR-Rec fusion module. The reconstruction branch generates the artifact-free image as prior, which is followed by a super-resolution branch for prior knowledge-guided super-resolution. Unlike the widely-used convolutional neural networks for extracting local features and Transformers with quadratic computational complexity for modeling long-range dependencies, we develop a new residual spatial-channel feature learning (RSCFL) module of two branches to efficiently establish feature relationships in spatial and channel dimensions. Moreover, the designed SR-Rec fusion module fuses the reconstruction prior and super-resolution features with each other in an adaptive manner. Our ICONet is built with multi-stage models to iteratively upscale the low-resolution images using steps of <inline-formula> <tex-math>${2 times }$</tex-math></inline-formula> and simultaneously interact between two branches in multi-stage supervisions. Quantitative and qualitative experimental results on the benchmarking dataset show that our ICONet outperforms most state-of-the-art approaches.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"827-838"},"PeriodicalIF":4.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336075","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

$L^{2}$FMamba: Lightweight Light Field Image Super-Resolution With State Space Model 轻量级光场图像超分辨率与状态空间模型

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-06 DOI: 10.1109/TCI.2025.3577338

Zeqiang Wei;Kai Jin;Zeyi Hou;Kuan Song;Xiuzhuang Zhou

{"title":"$L^{2}$FMamba: Lightweight Light Field Image Super-Resolution With State Space Model","authors":"Zeqiang Wei;Kai Jin;Zeyi Hou;Kuan Song;Xiuzhuang Zhou","doi":"10.1109/TCI.2025.3577338","DOIUrl":"https://doi.org/10.1109/TCI.2025.3577338","url":null,"abstract":"Transformers bring significantly improved performance to the light field image super-resolution task due to their long-range dependency modeling capability. However, the inherently high computational complexity of their core self-attention mechanism has increasingly hindered their advancement in this task. To address this issue, we first introduce the LF-VSSM block, a novel module inspired by progressive feature extraction, to efficiently capture critical long-range spatial-angular dependencies in light field images. LF-VSSM successively extracts spatial features within sub-aperture images, spatial-angular features between sub-aperture images, and spatial-angular features between light field image pixels. On this basis, we propose a lightweight network, <inline-formula><tex-math>$L^{2}$</tex-math></inline-formula>FMamba (Lightweight Light Field Mamba), which integrates the LF-VSSM block to leverage light field features for super-resolution tasks while overcoming the computational challenges of Transformer-based approaches. Extensive experiments on multiple light field datasets demonstrate that our method reduces the number of parameters and complexity while achieving superior super-resolution performance with faster inference speed.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"816-826"},"PeriodicalIF":4.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323022","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

Laser Ultrasonic Imaging Via the Time Domain Linear Sampling Method 基于时域线性采样方法的激光超声成像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-06 DOI: 10.1109/TCI.2025.3577405

Jian Song;Fatemeh Pourahmadian;Todd W. Murray;Venkatalakshmi V. Narumanchi

引用次数: 0

Robust Correspondence Imaging Against Random Disturbances With Single-Pixel Detection 基于单像素检测的抗随机干扰鲁棒对应成像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-06 DOI: 10.1109/TCI.2025.3577334

Zhihan Xu;Yin Xiao;Wen Chen

{"title":"Robust Correspondence Imaging Against Random Disturbances With Single-Pixel Detection","authors":"Zhihan Xu;Yin Xiao;Wen Chen","doi":"10.1109/TCI.2025.3577334","DOIUrl":"https://doi.org/10.1109/TCI.2025.3577334","url":null,"abstract":"Random disturbance has become a great challenge for correspondence imaging (CI) due to dynamic and nonlinear scaling factors. In this paper, we propose a robust CI against random disturbances for high-quality object reconstruction. To remove the effect of dynamic scaling factors induced by random disturbance, a wavelet and total variation (WATV) algorithm is developed to estimate a series of varying thresholds. Then, light intensities collected by a single-pixel detector are processed by using the series of estimated varying thresholds. To realize high-quality object reconstruction, the binarized light intensities and a series of random patterns are fed into a plug-and-play priors (PnP) algorithm with an iteration framework and a general denoiser, called as CI-PnP. Theoretical descriptions are given in detail to reveal the formation mechanism in CI under random disturbance. Optical measurements are conducted to verify robustness of the proposed CI against random disturbances. It is demonstrated that the proposed method can remove the effect of dynamic scaling factors induced by random disturbance, and can realize high-quality object reconstruction. The proposed method provides a promising solution to achieving ultra-high robustness against random disturbances in CI, and is promising in various applications.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"901-910"},"PeriodicalIF":4.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581738","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 Physics-Inspired Deep Learning Framework With Polar Coordinate Attention for Ptychographic Imaging 基于极坐标关注的物理启发深度学习框架

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-06-06 DOI: 10.1109/TCI.2025.3572250

Han Yue;Jun Cheng;Yu-Xuan Ren;Chien-Chun Chen;Grant A. van Riessen;Philip Heng Wai Leong;Steve Feng Shu

{"title":"A Physics-Inspired Deep Learning Framework With Polar Coordinate Attention for Ptychographic Imaging","authors":"Han Yue;Jun Cheng;Yu-Xuan Ren;Chien-Chun Chen;Grant A. van Riessen;Philip Heng Wai Leong;Steve Feng Shu","doi":"10.1109/TCI.2025.3572250","DOIUrl":"https://doi.org/10.1109/TCI.2025.3572250","url":null,"abstract":"Ptychographic imaging confronts inherent challenges in applying deep learning for phase retrieval from diffraction patterns. Conventional neural architectures, both convolutional neural networks and Transformer-based methods, are optimized for natural images with Euclidean spatial neighborhood-based inductive biases that exhibit geometric mismatch with the concentric coherent patterns characteristic of diffraction data in reciprocal space. In this paper, we present PPN, a physics-inspired deep learning network with Polar Coordinate Attention (PoCA) for ptychographic imaging, that aligns neural inductive biases with diffraction physics through a dual-branch architecture separating local feature extraction from non-local coherence modeling. It consists of a PoCA mechanism that replaces Euclidean spatial priors with physically consistent radial-angular correlations. PPN outperforms existing end-to-end models, with spectral and spatial analysis confirming its greater preservation of high-frequency details. Notably, PPN maintains robust performance compared to iterative methods even at low overlap ratios — well-suited for high-throughput imaging in real-world acquisition scenarios for samples with consistent structural characteristics.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"888-900"},"PeriodicalIF":4.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11027575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557938","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