IEEE Transactions on Computational Imaging最新文献

{"title":"F2IFlow for CT Metal Artifact Reduction","authors":"Jiandong Su;Ce Wang;Yinsheng Li;Dong Liang;Kun Shang","doi":"10.1109/TCI.2024.3485538","DOIUrl":"https://doi.org/10.1109/TCI.2024.3485538","url":null,"abstract":"Computed Tomography (CT) has been extensively utilized for medical diagnosis, assessment, as well as treatment planning and guidance. However, the image quality will be significantly compromised when metallic implants are present in patients' bodies, consequently affecting the clinical diagnosis or radiation therapy dose calculation. Previous Metal Artifact Reduction (MAR) methods either require prior knowledge about metallic implants or exhibit modeling bias in the mechanism of artifact formation, which restricts the capability to acquire high-quality CT images and increases the complexity of practical applications. In this paper, we propose a novel MAR method based on a feature-to-image conditional normalization flow, named F2IFlow, to address the problem. Specifically, we initially design an inherent feature extraction to get the inherent anatomical features of CT images. Then, a feature-to-image flow module is used for completing the metal-artifact-free CT images progressively through a series of reversible transformations. Incorporating these designs into F2IFlow, the coarse-to-fine strategy equips our model with the capability to deliver exceptional performance. Experimental results on both simulated and clinical datasets demonstrate that our method achieves superior performance in both quantitative and qualitative outcomes, exhibiting better visual effects in terms of artifact reduction and image fidelity.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1533-1546"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595134","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":"Extended Polar Format Algorithm for Non-Planar Target Imaging With DSM","authors":"Jingwei Chen;Daoxiang An;Dong Feng;Wu Wang;Zhimin Zhou","doi":"10.1109/TCI.2024.3490382","DOIUrl":"https://doi.org/10.1109/TCI.2024.3490382","url":null,"abstract":"In case of circular or non-linear acquisition trajectory, synthetic aperture radar (SAR) focusing becomes increasingly sensitive to elevation. For non-planar target imaging, it not only appears fore-shortening but also blurred. As the wider integration angle and higher elevation of objects, the defocus cannot be ignored. Generally, the polar format algorithm (PFA) is an efficient imaging algorithm for circular or non-linear SAR. However, in the process of PFA, the impact of focusing at an incorrect altitude has not been considered. In this article, the conventional PFA is adapted to incorporate the known digital surface model (DSM) into the imaging process. Firstly, the maximum allowable elevation deviation (MAED) \u0000<inline-formula><tex-math>$delta {{z}_{max }}$</tex-math></inline-formula>\u0000 is derived. Secondly, for non-planar targets that are higher than \u0000<inline-formula><tex-math>$delta {{z}_{max }}$</tex-math></inline-formula>\u0000, data extraction is applied in the range-Doppler domain. Additionally, a compensation function is multiplied, which is constructed based on DSM data separately. The corresponding original echo data is then replaced with the processed data. The whole method only involves fast Fourier transform (FFT) and complex multiplication which enhances operational efficiency. The simulated and experimental data results demonstrated the effectiveness and practicability of the proposed algorithm.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1602-1615"},"PeriodicalIF":4.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691801","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":"OccCasNet: Occlusion-Aware Cascade Cost Volume for Light Field Depth Estimation","authors":"Wentao Chao;Fuqing Duan;Xuechun Wang;Yingqian Wang;Ke Lu;Guanghui Wang","doi":"10.1109/TCI.2024.3488563","DOIUrl":"https://doi.org/10.1109/TCI.2024.3488563","url":null,"abstract":"Depth estimation using the Light Field (LF) technique is an essential task with a wide range of practical applications. While mainstream approaches based on multi-view stereo techniques can attain exceptional accuracy by creating finer cost volumes, they are resource-intensive, time-consuming, and often overlook occlusion during cost volume construction. To address these issues and strike a better balance between accuracy and efficiency, we propose an occlusion-aware cascade cost volume for LF depth (disparity) estimation. Our cascaded strategy reduces the sampling number while maintaining a constant sampling interval, enabling the construction of a finer cost volume. We also introduce occlusion maps to enhance accuracy in constructing the occlusion-aware cost volume. Specifically, we first generate a coarse disparity map through a coarse disparity estimation network. Then, we warp the sub-aperture images (SAIs) of adjacent views to the center view based on the coarse disparity map to generate occlusion maps for each SAI by photo-consistency constraints. Finally, we seamlessly incorporate occlusion maps into cascade cost volume to construct an occlusion-aware refined cost volume, allowing the refined disparity estimation network to yield a more precise disparity map. Extensive experiments demonstrate the effectiveness of our method. Compared with the state-of-the-art techniques, our method achieves a superior balance between accuracy and efficiency, ranking first in the Q25 metric on the HCI 4D benchmark.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1680-1691"},"PeriodicalIF":4.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761430","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":"GraphEIT: Unsupervised Graph Neural Networks for Electrical Impedance Tomography","authors":"Zixin Liu;Junwu Wang;Qianxue Shan;Dong Liu","doi":"10.1109/TCI.2024.3485517","DOIUrl":"https://doi.org/10.1109/TCI.2024.3485517","url":null,"abstract":"Convolutional Neural Networks (CNNs) based methodologies have found extensive application in Electrical Impedance Tomography (EIT). Convolution is commonly employed for uniform domains like pixel or voxel images. However, EIT reconstruction problem often involves nonuniform meshes, typically arising from finite element methods. Hence, reconciling nonuniform and uniform domains is essential. To address this issue, we propose an unsupervised reconstruction approach, termed GraphEIT, designed to tackle EIT problems directly on nonuniform mesh domains. The core concept revolves around representing conductivity via a fusion model that seamlessly integrates Graph Neural Networks (GNNs) and Multi-layer Perceptron networks (MLPs). Operating in an unsupervised manner eliminates the requirement for labeled data. Additionally, we incorporate Fourier feature projection to counter neural network spectral bias, thereby guiding the network to capture high-frequency details. Comprehensive experiments demonstrate the effectiveness of our proposed method, showcasing notable improvements in sharpness and shape preservation. Comparative analyses against state-of-the-art techniques underscore its superior convergence capability and robustness, particularly in the presence of measurement noise.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1559-1570"},"PeriodicalIF":4.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636496","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":"Fixed-Point Convergence of Multi-Block PnP ADMM and Its Application to Hyperspectral Image Restoration","authors":"Weijie Liang;Zhihui Tu;Jian Lu;Kai Tu;Michael K. Ng;Chen Xu","doi":"10.1109/TCI.2024.3485467","DOIUrl":"https://doi.org/10.1109/TCI.2024.3485467","url":null,"abstract":"Coupling methods of integrating multiple priors have emerged as a pivotal research focus in hyperspectral image (HSI) restoration. Among these methods, the Plug-and-Play (PnP) framework stands out and pioneers a novel coupling approach, enabling flexible integration of diverse methods into model-based approaches. However, the current convergence analyses of the PnP framework are highly unexplored, as they are limited to 2-block composite optimization problems, failing to meet the need of coupling modeling for incorporating multiple priors. This paper focuses on the convergence analysis of PnP-based algorithms for multi-block composite optimization problems. In this work, under the PnP framework and utilizing the alternating direction method of multipliers (ADMM) of the continuation scheme, we propose a unified multi-block PnP ADMM algorithm framework for HSI restoration. Inspired by the fixed-point convergence theory of the 2-block PnP ADMM, we establish a similar fixed-point convergence guarantee for the multi-block PnP ADMM with extended condition and provide a feasible parameter tuning methodology. Based on this framework, we design an effective mixed noise removal algorithm incorporating global, nonlocal and deep priors. Extensive experiments validate the algorithm's superiority and competitiveness.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1571-1587"},"PeriodicalIF":4.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636307","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":"CalibFPA: A Focal Plane Array Imaging System Based on Online Deep-Learning Calibration","authors":"Alper Güngör;M. Umut Bahceci;Yasin Ergen;Ahmet Sözak;O. Oner Ekiz;Tolga Yelboga;Tolga Çukur","doi":"10.1109/TCI.2024.3477312","DOIUrl":"https://doi.org/10.1109/TCI.2024.3477312","url":null,"abstract":"Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiplexed encoding of the visual scene is often attained via electronically controllable spatial light modulators (SLM). To capture system non-idealities such as optical aberrations, a system matrix is measured via additional offline scans, where the system response is recorded for a point source at each spatial location on the imaging grid. An HR image can then be reconstructed by solving an inverse problem that involves encoded measurements and the calibration matrix. However, this offline calibration framework faces limitations due to challenges in encoding single HR grid locations with a fixed coded aperture, lengthy calibration scans repeated to account for system drifts, and computational burden of reconstructions based on dense system matrices. Here, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA). To acquire multiplexed measurements, we devise an optical setup where a piezo-stage locomotes a pre-printed fixed coded aperture. We introduce a physics-driven deep-learning method to correct for the influences of optical aberrations in multiplexed measurements without the need for offline calibration scans. The corrected measurement matrix is of block-diagonal form, so it can be processed efficiently to recover HR images with a user-preferred reconstruction algorithm including least-squares, plug-and-play, or unrolled techniques. On simulated and experimental datasets, we demonstrate that CalibFPA outperforms state-of-the-art compressive FPA methods. We also report analyses to validate the design elements in CalibFPA and assess computational complexity.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1650-1663"},"PeriodicalIF":4.2,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736588","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":"Deep Motion Regularizer for Video Snapshot Compressive Imaging","authors":"Zan Chen;Ran Li;Yongqiang Li;Yuanjing Feng;Xingsong Hou;Xueming Qian","doi":"10.1109/TCI.2024.3477262","DOIUrl":"https://doi.org/10.1109/TCI.2024.3477262","url":null,"abstract":"Video snapshot compressive imaging (SCI) samples 3D high-speed video frames with temporally varying spatial modulation and compresses them into a single 2D measurement, and the SCI reconstruction algorithm aims to recover the original high-speed frames from the measurement. However, conventional video SCI systems encounter challenges when raising the frame rate of the reconstructed video. To achieve higher frame rates, the modulation mask's rate must be increased, which in turn leads to an increase in the associated hardware expenses. In this paper, we propose a deep unfolding-based reconstruction framework with optical flow for video SCI. The framework recovers both observed and unobserved frames from measurements, resulting in increased video frame rate. To estimate the optical flow, we transform the video features of the network into optical flow features during the iteration. Then, we design a deep denoiser and an optical flow-based motion regularizer combined with the voxels of coarse reconstructed frames to update the observed and unobserved frames. To improve the performance, we employ group convolution in the network and fuse the optical flow information from different phases to reduce the information loss. We further extend the proposed deep unfolding framework to the reconstruction of color SCI videos. Extensive experiments on benchmark data and real data prove that our proposed method has state-of-the-art reconstruction performance and visual effects.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1519-1532"},"PeriodicalIF":4.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524174","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":"Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction","authors":"Tao Hong;Xiaojian Xu;Jason Hu;Jeffrey A. Fessler","doi":"10.1109/TCI.2024.3477329","DOIUrl":"https://doi.org/10.1109/TCI.2024.3477329","url":null,"abstract":"Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a general framework that uses denoising algorithms as the prior or regularizer. Recent work showed that PnP methods with denoisers based on pretrained convolutional neural networks outperform other classical regularizers in CS MRI reconstruction. However, the numerical solvers for PnP can be slow for CS MRI reconstruction. This paper proposes a preconditioned PnP (\u0000<inline-formula><tex-math>$text{P}^{2}$</tex-math></inline-formula>\u0000nP) method to accelerate the convergence speed. Moreover, we provide proofs of the fixed-point convergence of the \u0000<inline-formula><tex-math>$text{P}^{2}$</tex-math></inline-formula>\u0000nP iterates. Numerical experiments on CS MRI reconstruction with non-Cartesian sampling trajectories illustrate the effectiveness and efficiency of the \u0000<inline-formula><tex-math>$text{P}^{2}$</tex-math></inline-formula>\u0000nP approach.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1476-1488"},"PeriodicalIF":4.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452742","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":"A Quantum Denoising-Based Resolution Enhancement Framework for 250-MHz and 500-MHz Quantitative Acoustic Microscopy","authors":"Sayantan Dutta;Jonathan Mamou","doi":"10.1109/TCI.2024.3473312","DOIUrl":"https://doi.org/10.1109/TCI.2024.3473312","url":null,"abstract":"Quantitative acoustic microscopy (QAM) forms two-dimensional (2D) quantitative maps of acoustic properties of thin tissue sections at a microscopic scale (\u0000<inline-formula><tex-math>$&lt; 8; mu$</tex-math></inline-formula>\u0000m) using very-high-frequency (i.e., \u0000<inline-formula><tex-math>$&gt;$</tex-math></inline-formula>\u0000 200 MHz) ultrasonic excitation. Our custom-made QAM systems employ a 250-MHz or a 500-MHz single-element transducer to produce 2D maps with theoretical spatial resolutions smaller than 8 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000m and 4 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000m, respectively. Even with the utilization of these state-of-the-art QAM instruments, spatial resolution still proves insufficient for certain clinical studies. However, designing a QAM system yielding finer resolution (i.e., using a higher-frequency transducer) is expensive and requires expert users. This work proposes a scheme to enhance the spatial resolution of the 2D QAM maps by exploiting an off-the-shelf quantum-based adaptive denoiser (DeQuIP), leveraging the principles of quantum many-body theory. Drawing upon the recent advancement in regularization-by-denoising (RED) for image restoration, we impose this external DeQuIP denoiser as a RED-prior coupled with an analytical solution to address the degradation operators in solving the QAM super-resolution problem. The efficiency of our proposed scheme is demonstrated by improving the resolution of experimental 2D acoustic-impedance maps (2DZMs) generated from data acquired using the 250-MHz and 500-MHz QAM systems. Our scheme demonstrates superior performance in recovering finer and subtle details with enhanced spatial resolution when applied to 2DZMs. For example, a spatial resolution improvement of 40% was achieved when applied to 2DZMs at 250-MHz, outperforming two other state-of-the-art methods, which only yielded 23–32% improvement. These observations highlight the efficacy of the proposed RED scheme.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1489-1504"},"PeriodicalIF":4.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518159","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":"TMSST-RID: A Target-Oriented SAR-ISAR Imaging Method Based on Synchrosqueezing","authors":"Zhifeng Xie;Lei Cui;Qingyuan Shen;Xiaoqing Wang;Peiqing Yang;Xingyi Su;Haifeng Huang","doi":"10.1109/TCI.2024.3468032","DOIUrl":"https://doi.org/10.1109/TCI.2024.3468032","url":null,"abstract":"Synthetic aperture radar (SAR) is an important high-resolution radar system for target surveillance. However, target motion causes a time-varying Doppler frequency in echo and defocused SAR images. To obtain a focused target image, the inverse SAR synthetic aperture radar (ISAR) algorithm is applied: the image is inverted back to the echo and focused using the ISAR imaging algorithm. Clutter significantly impacts the target focusing effect during the SAR-ISAR imaging process. To improve the SAR-ISAR imaging quality of ship targets in the presence of severe sea clutter interference, this article proposes a range instantaneous Doppler method based on the low-rank sparse decomposition and target-oriented multisynchrosqueezing transform (TMSST-RID), which can enhance the target while suppressing clutter. The target area is separated from the clutter area using the low-rank sparse decomposition method. Then, in the time–frequency (TF) analysis, our proposed TMSST method utilizes the prior of the target area to improve the signal-to-clutter ratio (SCR) of the SAR/ISAR image. Compared with the traditional TF transformation, the proposed TMSST enhances the target by sharpening the TF target representation while simultaneously avoiding clutter enhancement. The superiority of this SAR/ISAR imaging method is demonstrated in a performance evaluation using simulated and real radar data for ship targets in sea clutter.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1425-1438"},"PeriodicalIF":4.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383484","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}