IEEE Transactions on Computational Imaging最新文献

HDD-Net: Haar Dual Domain Network for Ring Artifacts Correction

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-04-01 DOI: 10.1109/TCI.2025.3551166

Xuelong Wu;Junsheng Wang;Qingjie Zhao

引用次数: 0

PACformer: A Multi-Stage Heterogeneous Convolutional-Vision Transformer for Sparse-View Photoacoustic Tomography Restoration

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-03-31 DOI: 10.1109/TCI.2025.3550716

Li He;Ruitao Chen;Xiangyu Liu;Xu Cao;Shouping Zhu;Yihan Wang

{"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}

引用次数: 0

Quick Unsupervised Hyperspectral Dimensionality Reduction for Earth Observation: A Comparison

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-03-28 DOI: 10.1109/TCI.2025.3555137

Daniela Lupu;Joseph L. Garrett;Tor Arne Johansen;Milica Orlandic;Ion Necoara

{"title":"Quick Unsupervised Hyperspectral Dimensionality Reduction for Earth Observation: A Comparison","authors":"Daniela Lupu;Joseph L. Garrett;Tor Arne Johansen;Milica Orlandic;Ion Necoara","doi":"10.1109/TCI.2025.3555137","DOIUrl":"https://doi.org/10.1109/TCI.2025.3555137","url":null,"abstract":"Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. In this work, eight unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection of methods. Our extensive experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"520-535"},"PeriodicalIF":4.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835450","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

Unsupervised Low-Dose CT Reconstruction With One-Way Conditional Normalizing Flows

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-03-19 DOI: 10.1109/TCI.2025.3553039

Ran An;Ke Chen;Hongwei Li

{"title":"Unsupervised Low-Dose CT Reconstruction With One-Way Conditional Normalizing Flows","authors":"Ran An;Ke Chen;Hongwei Li","doi":"10.1109/TCI.2025.3553039","DOIUrl":"https://doi.org/10.1109/TCI.2025.3553039","url":null,"abstract":"Deep-learning techniques have demonstrated significant potential in low-dose computed tomography (LDCT) reconstruction. Nevertheless, supervised methods are limited by the scarcity of labeled data in clinical scenarios, while CNN-based unsupervised denoising methods often result in excessive smoothing of reconstructed images. Although normalizing flows (NFs) based methods have shown promise in generating detail-rich images and avoiding over-smoothing, they face two key challenges: (1) Existing two-way transformation strategies between noisy images and latent variables, despite leveraging the regularization and generation capabilities of NFs, can lead to detail loss and secondary artifacts; and (2) Training NFs on high-resolution CT images is computationally intensive. While conditional normalizing flows (CNFs) can mitigate computational costs by learning conditional probabilities, current methods rely on labeled data for conditionalization, leaving unsupervised CNF-based LDCT reconstruction an unresolved challenge. To address these issues, we propose a novel unsupervised LDCT iterative reconstruction algorithm based on CNFs. Our approach implements a strict one-way transformation during alternating optimization in the dual spaces, effectively preventing detail loss and secondary artifacts. Additionally, we propose an unsupervised conditionalization strategy, enabling efficient training of CNFs on high-resolution CT images and achieving fast, high-quality unsupervised reconstruction. Experimental results across multiple datasets demonstrate that the proposed method outperforms several state-of-the-art unsupervised methods and even rivals some supervised approaches.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"485-496"},"PeriodicalIF":4.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792929","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

Wavenumber Domain 2-D Separable Data Reformatting Algorithm for High Squint Spotlight SAR

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-03-12 DOI: 10.1109/TCI.2025.3551164

Qianyu Deng;Yan Jiang;Xinhua Mao

引用次数: 0

Structured Illumination Microscopy With Uncertainty-Guided Deep Learning

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-03-12 DOI: 10.1109/TCI.2025.3550715

Xuyang Chang;Xiaoqin Zhu;Yibo Feng;Zhenyue Chen;Liheng Bian

{"title":"Structured Illumination Microscopy With Uncertainty-Guided Deep Learning","authors":"Xuyang Chang;Xiaoqin Zhu;Yibo Feng;Zhenyue Chen;Liheng Bian","doi":"10.1109/TCI.2025.3550715","DOIUrl":"https://doi.org/10.1109/TCI.2025.3550715","url":null,"abstract":"Super-resolution microscopy enables the visualization of subcellular structures with unprecedented detail, significantly advancing life sciences. Among the various techniques available, structured illumination microscopy (SIM) provides an ideal balance of speed, resolution, and phototoxicity. Recent advancements in deep learning have further enhanced SIM capabilities, achieving improved imaging quality with higher signal-to-noise ratios and fewer measurements. However, the opaque nature of these deep learning models complicates the quantification of uncertainty in their outputs, which may lead to visually appealing but scientifically inaccurate results, particularly challenging for clinical diagnostics. In this paper, we introduce a two-step strategy that not only quantifies the uncertainty of deep learning models but also enhances super-resolution reconstruction. The first step implements a novel sparse-constrained loss function, incorporating Jeffrey's prior, to accurately predict uncertainty maps. These maps assess the confidence levels of the network's predictions and identify potential inaccuracies. In the second step, these predicted uncertainty maps serve as an attention mechanism, directing the neural network's focus towards areas of high uncertainty to improve the reconstruction of high-frequency details and textures. A series of simulations and experiments confirm that our method accurately quantifies uncertainty and improves high-resolution image reconstruction, increasing the peak signal-to-noise ratio by an average of 1.7 dB and structural similarity by 0.06, compared to traditional methods on mitochondrial and microtubule datasets. Our approach holds promise for advancing the application of deep learning-based super-resolution microscopy in clinical settings.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"389-398"},"PeriodicalIF":4.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761376","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

Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-03-03 DOI: 10.1109/TCI.2025.3547265

K. Aditya Mohan;Massimiliano Ferrucci;Chuck Divin;Garrett A. Stevenson;Hyojin Kim

{"title":"Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction","authors":"K. Aditya Mohan;Massimiliano Ferrucci;Chuck Divin;Garrett A. Stevenson;Hyojin Kim","doi":"10.1109/TCI.2025.3547265","DOIUrl":"https://doi.org/10.1109/TCI.2025.3547265","url":null,"abstract":"4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an important inverse problem in non-destructive evaluation. Conventional back-projection based reconstruction methods assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. We use a forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued 4D object coordinates. Unlike previous neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"362-376"},"PeriodicalIF":4.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716387","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

Towards Robust and Generalizable Lensless Imaging With Modular Learned Reconstruction 利用模块化学习重建技术实现稳健、可通用的无透镜成像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-02-28 DOI: 10.1109/TCI.2025.3539448

Eric Bezzam;Yohann Perron;Martin Vetterli

{"title":"Towards Robust and Generalizable Lensless Imaging With Modular Learned Reconstruction","authors":"Eric Bezzam;Yohann Perron;Martin Vetterli","doi":"10.1109/TCI.2025.3539448","DOIUrl":"https://doi.org/10.1109/TCI.2025.3539448","url":null,"abstract":"Lensless cameras disregard the conventional design that imaging should mimic the human eye. This is done by replacing the lens with a thin mask, and moving image formation to the digital post-processing. State-of-the-art lensless imaging techniques use learned approaches that combine physical modeling and neural networks. However, these approaches make simplifying modeling assumptions for ease of calibration and computation. Moreover, the generalizability of learned approaches to lensless measurements of new masks has not been studied. To this end, we utilize a modular learned reconstruction in which a key component is a pre-processor prior to image recovery. We theoretically demonstrate the pre-processor's necessity for standard image recovery techniques (Wiener filtering and iterative algorithms), and through extensive experiments show its effectiveness for multiple lensless imaging approaches and across datasets of different mask types (amplitude and phase). We also perform the first generalization benchmark across mask types to evaluate how well reconstructions trained with one system generalize to others. Our modular reconstruction enables us to use pre-trained components and transfer learning on new systems to cut down weeks of tedious measurements and training. As part of our work, we open-source four datasets, and software for measuring datasets and for training our modular reconstruction.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"213-227"},"PeriodicalIF":4.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521502","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

Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread Functions

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-02-28 DOI: 10.1109/TCI.2025.3544019

Xiaolong Qian;Qi Jiang;Yao Gao;Shaohua Gao;Zhonghua Yi;Lei Sun;Kai Wei;Haifeng Li;Kailun Yang;Kaiwei Wang;Jian Bai

{"title":"Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread Functions","authors":"Xiaolong Qian;Qi Jiang;Yao Gao;Shaohua Gao;Zhonghua Yi;Lei Sun;Kai Wei;Haifeng Li;Kailun Yang;Kaiwei Wang;Jian Bai","doi":"10.1109/TCI.2025.3544019","DOIUrl":"https://doi.org/10.1109/TCI.2025.3544019","url":null,"abstract":"Controllable Depth-of-Field (DoF) imaging commonly produces amazing visual effects based on heavy and expensive high-end lenses. However, confronted with the increasing demand for mobile scenarios, it is desirable to achieve a lightweight solution with Minimalist Optical Systems (MOS). This work centers around two major limitations of MOS, i.e., the severe optical aberrations and uncontrollable DoF, for achieving single-lens controllable DoF imaging via computational methods. A Depth-aware Controllable DoF Imaging (DCDI) framework is proposed equipped with All-in-Focus (AiF) aberration correction and monocular depth estimation, where the recovered image and corresponding depth map are utilized to produce imaging results under diverse DoFs of any high-end lens via patch-wise convolution. To address the depth-varying optical degradation, we introduce a Depth-aware Degradation-adaptive Training (DA<inline-formula> <tex-math>$^{2}$</tex-math></inline-formula>T) scheme. At the dataset level, a Depth-aware Aberration MOS (DAMOS) dataset is established based on the simulation of Point Spread Functions (PSFs) under different object distances. Additionally, we design two plug-and-play depth-aware mechanisms to embed depth information into the aberration image recovery for better tackling depth-aware degradation. Furthermore, we propose a storage-efficient Omni-Lens-Field model to represent the 4D PSF library of various lenses. With the predicted depth map, recovered image, and depth-aware PSF map inferred by Omni-Lens-Field, single-lens controllable DoF imaging is achieved. To the best of our knowledge, we are the first to explore the single-lens controllable DoF imaging solution. Comprehensive experimental results demonstrate that the proposed framework enhances the recovery performance, and attains impressive single-lens controllable DoF imaging results, providing a seminal baseline for this field.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"305-320"},"PeriodicalIF":4.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611822","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

NLCMR: Indoor Depth Recovery Model With Non-Local Cross-Modality Prior

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-02-27 DOI: 10.1109/TCI.2025.3545358

Junkang Zhang;Zhengkai Qi;Faming Fang;Tingting Wang;Guixu Zhang

{"title":"NLCMR: Indoor Depth Recovery Model With Non-Local Cross-Modality Prior","authors":"Junkang Zhang;Zhengkai Qi;Faming Fang;Tingting Wang;Guixu Zhang","doi":"10.1109/TCI.2025.3545358","DOIUrl":"https://doi.org/10.1109/TCI.2025.3545358","url":null,"abstract":"Recovering a dense depth image from sparse inputs is inherently challenging. Image-guided depth completion has become a prevalent technique, leveraging sparse depth data alongside RGB images to produce detailed depth maps. Although deep learning-based methods have achieved notable success, many state-of-the-art networks operate as black boxes, lacking transparent mechanisms for depth recovery. To address this, we introduce a novel model-guided depth recovery method. Our approach is built on a maximum a posterior (MAP) framework and features an optimization model that incorporates a non-local cross-modality regularizer and a deep image prior. The cross-modality regularizer capitalizes on the inherent correlations between depth and RGB images, enhancing the extraction of shared information. Additionally, the deep image prior captures local characteristics between the depth and RGB domains effectively. To counter the challenge of high heterogeneity leading to degenerate operators, we have integrated an implicit data consistency term into our model. Our model is then realized as a network using the half-quadratic splitting algorithm. Extensive evaluations on the NYU-Depth V2 and SUN RGB-D datasets demonstrate that our method performs competitively with current deep learning techniques.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"265-276"},"PeriodicalIF":4.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570693","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