IEEE Transactions on Computational Imaging最新文献

{"title":"Multispectral and Hyperspectral Image Fusion With Spectrally Varying Blurs and MM Algorithm","authors":"Dan Pineau;François Orieux;Alain Abergel","doi":"10.1109/TCI.2025.3565138","DOIUrl":"https://doi.org/10.1109/TCI.2025.3565138","url":null,"abstract":"The fusion of multispectral and hyperspectral data allows for restoring data with enhanced spatial and spectral resolutions. In cases of varying spatial blur, the current approach is to solve an ill-posed inverse problem by minimizing a mixed criterion. This minimization commonly involves an iterative gradient-based method. This paper proposes a new algorithm based on the Majorize-Minimize approach to compute the minimizer of a semi-quadratic convex edge-preserving criterion. The proposition relies on a reachable explicit solution of the quadratic majorant without the need to solve a Sylvester equation and for which we developed the proof of existence that was missing in a previous work. We conduct experiments on realistic synthetic measurements for the James Webb Space Telescope and show that our proposed solutions outperform the state-of-the-art in both computation time, achieving a 7000-fold speedup with the closed-form solution, and reconstruction quality, with a 2 dB PSNR improvement for the MM-based solution.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"704-716"},"PeriodicalIF":4.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171033","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}

{"title":"High-Generalized Unfolding Model With Coupled Spatial-Spectral Transformer for Hyperspectral Image Reconstruction","authors":"Xian-Hua Han","doi":"10.1109/TCI.2025.3564776","DOIUrl":"https://doi.org/10.1109/TCI.2025.3564776","url":null,"abstract":"Deep unfolding framework has witnessed remarkable progress for hyperspectral image (HSI) reconstruction benefitting from advanced consolidation of the imaging model-driven and data-driven approaches, which are generally realized with the data reconstruction error term and the prior learning network. However, current methods still encounter challenges related to insufficient generalization and representation for the high-dimensional HSI data, manifesting in two key aspects: 1) assumption of the fixed sensing mask causing low generalization for reconstruction of the compressive measurements out of distribution; 2) imperfect prior representation network for the high-dimensional data in both spatial and spectral domains. To overcome the aforementioned issues, this study presents a high-generalized deep unfolding model using coupled spatial-spectral transformer (CS2Tr) for prior learning. Specifically, to improve the generalization capability, we synthesize the training samples with diverse masks to learn the unfolding model, and propose a mask guided-data modeling module for being incorporated with both data reconstruction term and prior learning network for degradation-aware updating and representation context modeling. To achieve robust prior representation, a coupled spatial-spectral transformer aiming at modeling both non-local spatial and spectral dependencies is introduced for capturing the 3D attributes of HSI. Moreover, we conduct the feature interaction among stages to capture rich and diverse contexts, and leverage the auxiliary losses on all stages for enhancing the recovery capability of each individual step. Extensive experiments on both simulated and real scenes have demonstrated that our proposed method outperforms the state-of-the-art HSI reconstruction approaches.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"625-637"},"PeriodicalIF":4.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072921","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}

{"title":"Single-Pixel Compressive Terahertz 3D Imaging","authors":"Adolphe Ndagijimana;Iñigo Ederra;Miguel Heredia Conde","doi":"10.1109/TCI.2025.3564161","DOIUrl":"https://doi.org/10.1109/TCI.2025.3564161","url":null,"abstract":"Terahertz (THz) imaging contends with the lack of cost-effective, off-the-shelf high-resolution array detectors and the slow acquisition speeds associated with pixel-by-pixel raster scanning. Single-pixel imaging with Compressive Sensing (CS) represents a potential solution for resolution and acquisition speed in a cost-efficient manner. Our paper introduces a novel approach for extending 2D single-pixel THz imaging systems to 3D using a single frequency. By leveraging the single-pixel approach, we achieve 3D resolution while avoiding mechanical scanning, and the use of a single frequency eliminates the need for bandwidth, a significant limitation of conventional techniques, where design of THz sources and detectors with large bandwidth remains challenging and typically complex. The Order Recursive Matching Pursuit (ORMP) algorithm is used as the sparse recovery method to exploit the sparsity/compressibility of the 3D THz signal and enable sampling at a rate far lower than that required by the Nyquist Theorem. The 2D sensing matrix is obtained by analyzing the diffracted propagation of THz imaging systems on a 2D surface perpendicular to the optical axis. Moreover, the 3D sensing matrix is based on the diffracted propagation of 2D surfaces at different sampling depth positions. Our system can quickly capture the reflective properties of every point in a 3D space using a single-pixel camera setup that leverages CS, making it a simple and efficient method for creating a fast 3D THz imaging system, particularly suited to high-frequency THz sources that operate efficiently at a single frequency or at small bandwidth.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"570-585"},"PeriodicalIF":4.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918671","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}

{"title":"Successive Optimization of Optics and Post-Processing With Differentiable Coherent PSF Operator and Field Information","authors":"Zheng Ren;Jingwen Zhou;Wenguan Zhang;Jiapu Yan;Bingkun Chen;Huajun Feng;Shiqi Chen","doi":"10.1109/TCI.2025.3564173","DOIUrl":"https://doi.org/10.1109/TCI.2025.3564173","url":null,"abstract":"Recently, the joint design of optical systems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult to fully represent the optical characteristics of complex, miniaturized lenses constrained by wavefront aberration or diffraction effects. In this work, we introduce a precise optical simulation model, and every operation in pipeline is differentiable. This model employs a novel initial value strategy to enhance the reliability of intersection calculation on high aspherics. Moreover, it utilizes a differential operator to reduce memory consumption during coherent point spread function calculations. To efficiently address various degradation, we design a joint optimization procedure that leverages field information. Guided by a general restoration network, the proposed method not only enhances the image quality, but also successively improves the optical performance across multiple lenses that are already in professional level. This joint optimization pipeline offers innovative insights into the practical design of sophisticated optical systems and post-processing algorithms.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"599-608"},"PeriodicalIF":4.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937961","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}

{"title":"Score-Based Generative Model With Conditional Null-Space Learning for Limited-Angle Tomographic Reconstruction in Medical Imaging","authors":"Genyuan Zhang;Zihao Wang;Haijun Yu;Song Ni;Haixia Xie;Qiegen Liu;Fenglin Liu;Shaoyu Wang","doi":"10.1109/TCI.2025.3562059","DOIUrl":"https://doi.org/10.1109/TCI.2025.3562059","url":null,"abstract":"Limited-angle computed tomography (LA-CT) reconstruction represents a typically ill-posed inverse problem, frequently resulting in reconstructions with noticeable edge divergence and missing features. Score-based generative models (SGMs) based reconstruction methods have shown strong ability to reconstruct high-fidelity images for LA-CT. Data consistency is crucial for generating reliable and high-quality results in SGMs-based reconstruction methods. However, existing deep reconstruction methods have not fully explored data consistency, resulting in suboptimal performance. Based on this, we proposed a Conditional Score-based Null-space (CSN) generative model for LA-CT reconstruction. First, CSN integrates prior physical information of limited-angle scanning as conditional constraint, which can enable SGMs to obtain more accurate generation. Second, in order to balance the consistency and realness of the reconstruction results, the range-null space decomposition strategy is introduced in the sampling process. This strategy ensures that the estimation of the information occurs only in the null-space. Finally, we employ the sparse least square (LSQR) instead of commonly used consistency terms such as simultaneous iterative reconstruction technique (SIRT), thereby achieving superior reconstruction results. In addition, a mathematical convergence analysis of our CSN method is provided. Experimental evaluations on both numerical simulations and real-world datasets demonstrate that the proposed method offers notable advantages in reconstruction quality.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"556-569"},"PeriodicalIF":4.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918784","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}

{"title":"Diff-Holo: A Residual Diffusion Model With Complex Transformer for Rapid Single-Frame Hologram Reconstruction","authors":"Ziqi Bai;Xianming Liu;Cheng Guo;Kui Jiang;Junjun Jiang;Xiangyang Ji","doi":"10.1109/TCI.2025.3561683","DOIUrl":"https://doi.org/10.1109/TCI.2025.3561683","url":null,"abstract":"Deep learning approaches have gained significant traction in holographic imaging, with diffusion models—an emerging class of deep generative models—showing particular promise in hologram reconstruction. Unlike conventional neural networks that directly generate outputs, diffusion models gradually add noise to data and train neural networks to remove it, enabling them to learn implicit priors of the underlying data distribution. However, current diffusion-based hologram reconstruction methods often require hundreds or even thousands of iterations to achieve high-fidelity results, leading to processing times of several minutes or more—falling short of the fast imaging demands of holographic systems. To address this, we propose <italic>Diff-Holo</i>, a residual diffusion model integrated with a complex transformer, designed for rapid and high-quality single-frame hologram reconstruction. Specifically, we create a shorter and more efficient Markov chain by controlling the residuals between clean images and those degraded by twin-image artifacts. Additionally, we incorporate complex-valued priors into the network by using a complex window-based transformer as the backbone, enhancing the network's ability to process complex-valued data in the reverse reconstruction process. Experimental results demonstrate that Diff-Holo achieves high-quality single-frame reconstructions in as few as 15 sampling steps, reducing reconstruction time from minutes to under 2.2 seconds.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"689-703"},"PeriodicalIF":4.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171030","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}

{"title":"Multi-Scale Cascaded With Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction","authors":"Peng Yuan;Fei Lyu;Zhiqiang Gao;Chunfeng Yang;Dianlin Hu;Jian Zhu;Zhan Wu;Tianling Lyu;Wei Zhao;Jianmin Dong;Yang Chen","doi":"10.1109/TCI.2025.3561660","DOIUrl":"https://doi.org/10.1109/TCI.2025.3561660","url":null,"abstract":"Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motion-compensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSC-Net), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"717-731"},"PeriodicalIF":4.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171032","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}

{"title":"PAH2T-Former: Paired-Attention Hybrid Hierarchical Transformer for Synergistically Enhanced FMT Reconstruction Quality and Efficiency","authors":"Peng Zhang;Xingyu Liu;Qianqian Xue;Yu Shang;Chen Liu;Ruhao Chen;Honglei Gao;Jiye Liang;Wenjian Wang;Guanglei Zhang","doi":"10.1109/TCI.2025.3559431","DOIUrl":"https://doi.org/10.1109/TCI.2025.3559431","url":null,"abstract":"Fluorescence molecular tomography (FMT) is a sensitive optical imaging technique that can achieve three-dimensional (3D) tomographic images at the molecular and cellular levels. However, reconstructing the internal 3D distribution of fluorescent targets from surface two-dimensional (2D) fluorescence projection data remains a challenging task. In recent years, deep learning-based FMT reconstruction has received considerable attention, demonstrating superior performance compared to conventional methods, particularly combined with Transformers. Unlike convolutional architectures that emphasize local context, Transformers leverage self-attention mechanisms to excel at capturing long-range dependencies, thereby enhancing FMT reconstruction accuracy. Nevertheless, the quadratic computational complexity of self-attention poses a bottleneck, particularly pertinent in 3D FMT reconstructions. This paper aims to propose a novel Transformer-based FMT reconstruction algorithm that not only delivers high-quality reconstruction accuracy but also maintains excellent performance in efficiency and inference speed. The key design involves introducing a novel Spatial-Channel Paired Attention Module (SC-PAM), which employs a pair of interdependent branches based on spatial and channel attention, thus effectively learn discriminative features in both spatial and channel domains, meanwhile exhibiting linear complexity relative to the input projection size. Furthermore, to facilitate data transmission between the spatial and channel branches, we share the weights of the query and key mapping functions, which provides a complementary paired attention without elevating complexity. Extensive evaluations through numerical simulations and in vivo experiments were performed to validate effectiveness of the proposed model. The results show that our PAH2T-Former method achieves the highest Dice while reducing model parameters and complexity.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"536-545"},"PeriodicalIF":4.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875176","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}

{"title":"HDD-Net: Haar Dual Domain Network for Ring Artifacts Correction","authors":"Xuelong Wu;Junsheng Wang;Qingjie Zhao","doi":"10.1109/TCI.2025.3551166","DOIUrl":"https://doi.org/10.1109/TCI.2025.3551166","url":null,"abstract":"Ring artifacts are common artifacts in X-ray Computed Tomography (XCT) scans and have a significant impact on subsequent feature/phase extractions due to the small grayscale gradients in XCT volume data of bulk materials. This paper proposes the Haar Dual Domain Network for correcting ring artifacts. By utilizing the Haar wavelet decomposition on images containing ring artifacts in both the image and projection domains, the ring artifacts are preliminarily separated, facilitating their removal by neural networks while preserving microstructure features such as low-contrast phase boundaries. By constructing a feature fusion network, the information from both 2D slices and 3D projection volume data has been fully integrated to eliminate ring artifacts while preserving the edges of every feature. The effectiveness of the Haar wavelet transform and fusion network has been validated by ablation experiments, proving the application of HDD-Net to large volume of XCT data.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"399-409"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761377","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}

{"title":"PACformer: A Multi-Stage Heterogeneous Convolutional-Vision Transformer for Sparse-View Photoacoustic Tomography Restoration","authors":"Li He;Ruitao Chen;Xiangyu Liu;Xu Cao;Shouping Zhu;Yihan Wang","doi":"10.1109/TCI.2025.3550716","DOIUrl":"https://doi.org/10.1109/TCI.2025.3550716","url":null,"abstract":"Sparse sampling of photoacoustic (PA) signals is a crucial strategy for enhancing the feasibility of photoacoustic tomography (PAT) in clinical settings by reducing system complexity and costs. However, this approach often faces significant artifacts resulting from traditional reconstruction algorithms, underscoring the urgent need for effective solutions. To address the critical challenge of balancing computational efficiency with imaging quality, we introduce PACformer—a novel hybrid model that integrates convolutional neural networks (CNNs) with multi-head self-attentions (MSAs) to improve the reconstruction of sparse-view PAT images. While conventional CNNs excel at local feature extraction, they often struggle to capture long-range dependencies inherent in continuous structures and the diverse artifact patterns present in PAT images. PACformer tackles these limitations through a dual architecture that seamlessly combines MSAs with heterogeneous convolutional layers. Since feature representations differ in size and semantics at various stages of the deep model, PACformer employs specialized blocks for shallow and deep stages. Specifically, it utilizes efficient local convolutions and windowed MSAs for high-resolution feature maps, conditional convolutions (CondConv) integrated with MSAs for advanced feature representation in deeper stages, and Scale-Modulated Convolution combined with CondConv for the bottleneck stage. Experimental results on open-source datasets demonstrate PACformer's superior performance compared to traditional and state-of-the-art networks, validated through ablation studies and attention map visualizations. By effectively modeling both local and global artifacts, PACformer establishes itself as a robust solution for sparse-view PAT reconstruction.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"377-388"},"PeriodicalIF":4.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761353","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}