IEEE Transactions on Computational Imaging最新文献_第2页

Learning Light Field Denoising With Symmetrical Refocusing Strategy

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-27 DOI: 10.1109/TCI.2024.3507642

Song Chang;Youfang Lin;Wenqi Wang;Da An;Shuo Zhang

{"title":"Learning Light Field Denoising With Symmetrical Refocusing Strategy","authors":"Song Chang;Youfang Lin;Wenqi Wang;Da An;Shuo Zhang","doi":"10.1109/TCI.2024.3507642","DOIUrl":"https://doi.org/10.1109/TCI.2024.3507642","url":null,"abstract":"Due to hardware restrictions, Light Field (LF) images are often captured with heavy noise, which seriously obstructs the subsequent LF applications. In this paper, we propose a novel symmetrical refocusing strategy to construct the focal stack for every view in LF images and design a simple learning-based framework for LF denoising. Specifically, we first select views that are symmetrically arranged around a target view in LF images. Then we shift and average the selected views to calculate the focal stack, in which all refocused images are aligned with the target view and the noises are effectively suppressed. Then, a Fusion Network is designed to fuse the sharp regions in the focal stack to obtain the denoised target view with sharp details. We further exploit more angular and spatial detail information in LF images and combine the fusion outputs to obtain the final denoised LF images. We evaluate our method in various noise levels and kinds of noisy LF images with different disparity ranges. The experiments show that our method achieves the highest quality in both qualitative and quantitative evaluation than state-of-the-art methods. The proposed symmetrical refocusing strategy is also verified to highly improve the denoising performances.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1786-1798"},"PeriodicalIF":4.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821174","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

An Adaptive Photon Count Reconstruction Algorithm for Sparse Count and Strong Noise Count Data With Low Signal Background Ratio

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-27 DOI: 10.1109/TCI.2024.3507647

Meijun Chen;Zhendong Shi;Wei Chen;Fangjie Xu;Yong Jiang;Yijiang Mao;Shiyue Xu;Bowen Chen;Yalan Wang;Zecheng Wang;Jie Leng

{"title":"An Adaptive Photon Count Reconstruction Algorithm for Sparse Count and Strong Noise Count Data With Low Signal Background Ratio","authors":"Meijun Chen;Zhendong Shi;Wei Chen;Fangjie Xu;Yong Jiang;Yijiang Mao;Shiyue Xu;Bowen Chen;Yalan Wang;Zecheng Wang;Jie Leng","doi":"10.1109/TCI.2024.3507647","DOIUrl":"https://doi.org/10.1109/TCI.2024.3507647","url":null,"abstract":"Single-photon lidar detection data in applications can show different characteristics: sparse count data and strong noise count data with low signal-to-background ratio (SBR), making it difficult to accurately reconstruct depth and intensity information. The existing statistical-based algorithms can achieve reconstruction, but they may lack compatibility for sparse counting and strong noise counting cases which will switch to each other in practical applications. In this paper, an adaptive photon count reconstruction algorithm for sparse count and strong noise count data with low SBR is proposed based on the difference in temporal distribution characteristics between the echo and noise count data. The aggregation characteristic of echo count data in time dimension is proposed to adaptively separate the echo and noise regions in the histogram to reduce the noise interference, and based on the relative difference between count levels in the time neighborhood, an objective function is constructed to reconstruct depth and intensity using optimization. The reconstruction results based on simulated and experimental data confirm that the reconstruction accuracies under both sparse counting and strong noise counting cases are effectively improved under low SBR conditions. Compared with the state-of-the-art algorithms, the depth absolute error is reduced by nearly 50%, the edge error is reduced by an order of magnitude and the proportion of correctly reconstructed pixels reaches 90% when SBR = 0.1. It shows the potential of the proposed algorithm for improving target recognition ability and all-day imaging.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1799-1814"},"PeriodicalIF":4.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10768988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142843065","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

Full-Wave Simulations of Tomographic Optical Imaging Through Augmented Partial Factorization

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-22 DOI: 10.1109/TCI.2024.3499747

Zeyu Wang;Yiwen Zhang;Chia Wei Hsu

{"title":"Full-Wave Simulations of Tomographic Optical Imaging Through Augmented Partial Factorization","authors":"Zeyu Wang;Yiwen Zhang;Chia Wei Hsu","doi":"10.1109/TCI.2024.3499747","DOIUrl":"https://doi.org/10.1109/TCI.2024.3499747","url":null,"abstract":"Label-free optical imaging inside scattering media is important for many disciplines. One challenge is that the ground-truth structure is often unknown, so one cannot rigorously assess and compare different imaging schemes. Full-wave simulations can address this issue, but the heavy computing cost has restricted them to small, typically weakly scattering, systems. Here we use a recently introduced “augmented partial factorization” method to enable full-wave simulations of tomographic optical imaging deep inside multiple-scattering media. We also provide a unifying framework that models different scattering-based imaging methods including reflectance confocal microscopy, optical coherence tomography and microscopy, interferometric synthetic aperture microscopy, and the recently proposed scattering matrix tomography in the same virtual setup, so they can be directly compared to the ground truth and against each other. The ground truth enables the identification of artifacts that would typically be mistaken as being correct while setting a rigorous and uniform standard across different methods. By leveraging the latest advances in computational electromagnetics, this work brings the power, versatility, and convenience of full-wave modeling to deep imaging in the multiple-scattering regime.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1775-1785"},"PeriodicalIF":4.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10764744","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821175","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

Relaxation-Based Super-Resolution Method in Pulsed Magnetic Particle Imaging

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-20 DOI: 10.1109/TCI.2024.3503364

Lei Li;Haohao Yan;Yuge Li;Yidong Liao;Yanjun Liu;Ruili Zhang;Zhongliang Wang;Xin Feng;Jie Tian

{"title":"Relaxation-Based Super-Resolution Method in Pulsed Magnetic Particle Imaging","authors":"Lei Li;Haohao Yan;Yuge Li;Yidong Liao;Yanjun Liu;Ruili Zhang;Zhongliang Wang;Xin Feng;Jie Tian","doi":"10.1109/TCI.2024.3503364","DOIUrl":"https://doi.org/10.1109/TCI.2024.3503364","url":null,"abstract":"Spatial resolution is one of the most critical indicators for magnetic particle imaging (MPI). Due to factors such as relaxation effects and suboptimal magnetization response, MPI has not yet reached the promised spatial resolution. Pulsed MPI is a method that enables MPI to achieve the resolution predicted by the Langevin function, which thereby enables larger magnetic particles (MNPs) to enhance resolution. To further exceed this resolution, we propose a relaxation-based super-resolution method which leverages the principle that MNPs at different positions exhibit varying relaxation times due to the different DC fields provided by the gradient field. This principle allows the super-resolution method to extract signals from the center of the field free region (FFR) to enhance spatial resolution. The super-resolution method first truncates the exponential decay signal during the plateau phase of the excitation field. Then, the truncated signals are decomposed based on their relaxation times. Finally, signals from the center position of the FFR are retained, and signals from the periphery of the FFR are discarded. Using this retained signal for reconstruction results in a higher spatial resolution. We validate this method via both simulation and experimental measurements. The results indicate that, compared with sinusoidal MPI and pulsed MPI without super-resolution, the super-resolution method has two-fold improvement in resolution.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1692-1705"},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789117","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

An Efficient Method for Modelling Millimeter-Wave Scan for Security Screening of Humans 用于人体安全检查的毫米波扫描建模的高效方法

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-15 DOI: 10.1109/TCI.2024.3487393

Wenyi Shao;Yan Li

引用次数: 0

Multi-Layered Surface Estimation for Low-Cost Optical Coherence Tomography

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-13 DOI: 10.1109/TCI.2024.3497602

Joshua Rapp;Hassan Mansour;Petros Boufounos;Toshiaki Koike-Akino;Kieran Parsons

{"title":"Multi-Layered Surface Estimation for Low-Cost Optical Coherence Tomography","authors":"Joshua Rapp;Hassan Mansour;Petros Boufounos;Toshiaki Koike-Akino;Kieran Parsons","doi":"10.1109/TCI.2024.3497602","DOIUrl":"https://doi.org/10.1109/TCI.2024.3497602","url":null,"abstract":"Optical coherence tomography (OCT) has broad applicability for 3D sensing, such as reconstructing the surface profiles of multi-layered samples in industrial settings. However, accurately determining the number of layers and their precise locations is a challenging task, especially for low-cost OCT systems having low signal-to-noise ratio (SNR). This paper introduces a principled and noise-robust method of detection and estimation of surfaces measured with OCT. We first derive the maximum likelihood estimator (MLE) for the position and reflectivity of a single opaque surface. We next derive a threshold that uses the acquisition noise variance and the number of measurements available to set a target probability for false acceptance of spurious surface estimates. The threshold and MLE are then incorporated into an algorithm that sequentially detects and estimates surface locations. We demonstrate reconstruction of fine details in samples with optical path lengths around 1 mm and depth error down to 1.5 \u0000<inline-formula><tex-math>$mathrm{mu }$</tex-math></inline-formula>\u0000m despite SNRs as low as –10 dB.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1706-1721"},"PeriodicalIF":4.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789047","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

Data-Driven Discovery of Mechanical Models Directly From MRI Spectral Data 直接从核磁共振成像光谱数据中发现机械模型的数据驱动型方法

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-13 DOI: 10.1109/TCI.2024.3497775

David G. J. Heesterbeek;Max H. C. van Riel;Tristan van Leeuwen;Cornelis A. T. van den Berg;Alessandro Sbrizzi

{"title":"Data-Driven Discovery of Mechanical Models Directly From MRI Spectral Data","authors":"David G. J. Heesterbeek;Max H. C. van Riel;Tristan van Leeuwen;Cornelis A. T. van den Berg;Alessandro Sbrizzi","doi":"10.1109/TCI.2024.3497775","DOIUrl":"https://doi.org/10.1109/TCI.2024.3497775","url":null,"abstract":"Finding interpretable biomechanical models can provide insight into the functionality of organs with regard to physiology and disease. However, identifying broadly applicable dynamical models for in vivo tissue remains challenging. In this proof of concept study we propose a reconstruction framework for data-driven discovery of dynamical models from experimentally obtained undersampled MRI spectral data. The method makes use of the previously developed spectro-dynamic framework which allows for reconstruction of displacement fields at high spatial and temporal resolution required for model identification. The proposed framework combines this method with data-driven discovery of interpretable models using Sparse Identification of Non-linear Dynamics (SINDy). The design of the reconstruction algorithm is such that a symbiotic relation between the reconstruction of the displacement fields and the model identification is created. Our method does not rely on periodicity of the motion. It is successfully validated using spectral data of a dynamic phantom gathered on a clinical MRI scanner. The dynamic phantom is programmed to perform motion adhering to 5 different (non-linear) ordinary differential equations. The proposed framework performed better than a 2-step approach where the displacement fields were first reconstructed from the undersampled data without any information on the model, followed by data-driven discovery of the model using the reconstructed displacement fields. This study serves as a first step in the direction of data-driven discovery of in vivo models.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1640-1649"},"PeriodicalIF":4.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736529","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

INF3: Implicit Neural Feature Fusion Function for Multispectral and Hyperspectral Image Fusion INF3：用于多光谱和高光谱图像融合的隐式神经特征融合函数

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-11 DOI: 10.1109/TCI.2024.3488569

Ruo-Cheng Wu;Shangqi Deng;Ran Ran;Hong-Xia Dou;Liang-Jian Deng

{"title":"INF3: Implicit Neural Feature Fusion Function for Multispectral and Hyperspectral Image Fusion","authors":"Ruo-Cheng Wu;Shangqi Deng;Ran Ran;Hong-Xia Dou;Liang-Jian Deng","doi":"10.1109/TCI.2024.3488569","DOIUrl":"https://doi.org/10.1109/TCI.2024.3488569","url":null,"abstract":"Multispectral and Hyperspectral Image Fusion (MHIF) is a task that aims to fuse a high-resolution multispectral image (HR-MSI) and a low-resolution hyperspectral image (LR-HSI) acquired on the same scene to obtain a high-resolution hyperspectral image (HR-HSI). Benefiting from the powerful inductive bias capability, convolutional neural network (CNN) based methods have achieved great success for the MHIF task. However, they lack flexibility when processing multi-scale images and require convolution structures be stacked to enhance performance. Implicit neural representation (INR) has recently achieved good performance and interpretability in 2D processing tasks thanks to its ability to locally interpolate samples and utilize multimodal content, such as pixels and coordinates. Although INR-based approaches show promising results, they put additional demands on high-frequency information (e.g., positional encoding). In this paper, we propose the use of the HR-MSI as high-frequency detail auxiliary input, thus introducing a new INR-based hyperspectral fusion function called implicit neural feature fusion function (INF\u00003\u0000). The method overcomes the inherent shortcomings of vanilla INR thereby solving the MHIF problem. Specifically, our INF\u00003\u0000 designs a dual high-frequency fusion (DHFF) structure that obtains high-frequency information from HR-MSI and LR-HSI fusing them with coordinate information. Moreover, the proposed INF\u00003\u0000 incorporates a parameter-free method called INR with cosine similarity (INR-CS) that uses cosine similarity to generate local weights through feature vectors. Relied upon INF\u00003\u0000, we build an implicit neural fusion network (INFN) that achieves state-of-the-art performance for the MHIF task on two public datasets, i.e., CAVE and Harvard. It also reaches the advanced level on the Pansharpening task, proving the flexibility of the proposed approach.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1547-1558"},"PeriodicalIF":4.2,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600116","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

Recurrent Cross-Modality Fusion for Time-of-Flight Depth Denoising 用于飞行时间深度去噪的递归跨模态融合技术

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-11 DOI: 10.1109/TCI.2024.3496312

Guanting Dong;Yueyi Zhang;Xiaoyan Sun;Zhiwei Xiong

{"title":"Recurrent Cross-Modality Fusion for Time-of-Flight Depth Denoising","authors":"Guanting Dong;Yueyi Zhang;Xiaoyan Sun;Zhiwei Xiong","doi":"10.1109/TCI.2024.3496312","DOIUrl":"https://doi.org/10.1109/TCI.2024.3496312","url":null,"abstract":"The widespread use of Time-of-Flight (ToF) depth cameras in academia and industry is limited by noise, such as Multi-Path-Interference (MPI) and shot noise, which hampers their ability to produce high-quality depth images. Learning-based ToF denoising methods currently in existence often face challenges in delivering satisfactory performance in complex scenes. This is primarily attributed to the impact of multiple reflected signals on the formation of MPI, rendering it challenging to predict MPI directly through spatially-varying convolutions. To address this limitation, we adopt a recurrent architecture that exploits the prior that MPI is decomposable into an additive combination of the geometric information for the neighboring pixels. Our approach employs a Gated Recurrent Unit (GRU) based network to estimate a long-distance aggregation process, simplifying the MPI removal and updating depth correction over multiple steps. Additionally, we introduce a global restoration module and a local update module to fuse depth and amplitude features, which improves denoising performance and prevents structural distortions. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our approach over state-of-the-art methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1626-1639"},"PeriodicalIF":4.2,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713879","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

Equilibrium Model With Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging 磁粒子成像中基于模型重建的各向异性平衡模型

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-11-01 DOI: 10.1109/TCI.2024.3490381

Marco Maass;Tobias Kluth;Christine Droigk;Hannes Albers;Konrad Scheffler;Alfred Mertins;Tobias Knopp

{"title":"Equilibrium Model With Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging","authors":"Marco Maass;Tobias Kluth;Christine Droigk;Hannes Albers;Konrad Scheffler;Alfred Mertins;Tobias Knopp","doi":"10.1109/TCI.2024.3490381","DOIUrl":"https://doi.org/10.1109/TCI.2024.3490381","url":null,"abstract":"Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolution. To reconstruct an image of the tracer concentration, the magnetization dynamics of the particles must be accurately modeled. A popular ensemble model is based on solving the Fokker-Plank equation, taking into account either Brownian or Néel dynamics. The disadvantage of this model is that it is computationally expensive due to an underlying stiff differential equation. A simplified model is the equilibrium model, which can be evaluated directly but in most relevant cases it suffers from a non-negligible modeling error. In the present work, we investigate an extended version of the equilibrium model that can account for particle anisotropy. We show that this model can be expressed as a series of Bessel functions, which can be truncated based on a predefined accuracy, leading to very short computation times, which are about three orders of magnitude lower than equivalent Fokker-Planck computation times. We investigate the accuracy of the model for 2D Lissajous magnetic particle imaging sequences and show that the difference between the Fokker-Planck and the equilibrium model with anisotropy is sufficiently small so that the latter model can be used for image reconstruction on experimental data with only marginal loss of image quality, even compared to a system matrix-based reconstruction.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1588-1601"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691673","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