样式: 排序: IF: - GO 导出 标记为已读
-
Domain Expansion via Network Adaptation for Solving Inverse Problems IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-03-13 Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
-
PRNet: Pyramid Restoration Network for RAW Image Super-Resolution IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-03-12 Mingyang Ling, Kan Chang, Mengyuan Huang, Hengxin Li, Shuping Dang, Baoxin Li
-
Single-Image HDR Reconstruction Assisted Ghost Suppression and Detail Preservation Network for Multi-Exposure HDR Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-03-11 Huafeng Li, Zhenmei Yang, Yafei Zhang, Dapeng Tao, Zhengtao Yu
The reconstruction of high dynamic range (HDR) images from multi-exposure low dynamic range (LDR) images in dynamic scenes presents significant challenges, especially in preserving and restoring information in oversaturated regions and avoiding ghosting artifacts. However, current methods often struggle to address these challenges. To this end, our work aims to bridge this gap by developing a multi-exposure
-
TPU Based Deep Learning Image Enhancement for Real-time Point-of-care Ultrasound IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-03-08 Ouwen Huang, Mark L. Palmeri
-
Hierarchical Edge Refinement Network for Guided Depth Map Super-Resolution IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-03-06 Shuo Zhang, Zexu Pan, Yichang Lv, Youfang Lin
-
Coupling Model- and Data-Driven Networks for CT Metal Artifact Reduction IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-29 Baoshun Shi, Shaolei Zhang, Ke Jiang, Qiusheng Lian
Computed tomography (CT) images are often corrupted by undesirable artifacts due to the presence of metallic implants, creating the problem of metal artifact reduction (MAR). Existing deep learning-based efforts of tackling this problem share two main common limitations, limiting their practical applications. Firstly, single domain knowledge is insufficient for MAR task, since image domain networks
-
EgeFusion: Towards Edge Gradient Enhancement in Infrared and Visible Image Fusion With Multi-Scale Transform IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-27 Haojie Tang, Gang Liu, Yao Qian, Jiebang Wang, Jinxin Xiong
Existing image fusion methods focus on aggregate image features from different modalities into a clear and comprehensive image. However, these solutions ignore the importance of gradient features, which results in smooth performance of contrast information in the fused images. In this paper, an edge gradient enhancement method for infrared and visible image fusion is proposed, named EgeFusion. First
-
The Secrets of Non-Blind Poisson Deconvolution IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-27 Abhiram Gnanasambandam, Yash Sanghvi, Stanley H. Chan
Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise. In photon-limited conditions, however, the excessive amount of shot noise makes traditional deconvolution algorithms fail. In searching for reasons why these methods fail, we present a systematic analysis of the Poisson non-blind deconvolution algorithms reported in the
-
Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-26 Carter Lyons, Raghu G. Raj, Margaret Cheney
Incorporating prior information into inverse problems, e.g. via maximum-a-posteriori estimation, is an important technique for facilitating robust inverse problem solutions. In this paper, we devise two novel approaches for linear inverse problems that permit problem-specific statistical prior selections within the compound Gaussian (CG) class of distributions. The CG class subsumes many commonly used
-
A Two-Stream Stacked Autoencoder With Inter-Class Separability for Bilinear Hyperspectral Unmixing IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-23 Chunhong Cao, Wei Song, Han Xiang, Hongbo Yi, Fen Xiao, Xieping Gao
Deep learning-based hyperspectral unmixing (HU) is getting increasing attention in the field of remote sensing, aiming at endmember extraction and abundance estimation at pixel scale. However, many existing deep learning-based unmixing methods base on linear mixing models, neglecting complex nonlinear light scattering interactions. Furthermore, these methods often treat all spectral bands indiscriminately
-
A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-23 Tao Hong, Luis Hernandez-Garcia, Jeffrey A. Fessler
Model-based methods are widely used for reconstruction in compressed sensing (CS) magnetic resonance imaging (MRI), using regularizers to describe the images of interest. The reconstruction process is equivalent to solving a composite optimization problem. Accelerated proximal methods (APMs) are very popular approaches for such problems. This paper proposes a complex quasi-Newton proximal method (CQNPM)
-
A Computationally Light MUSIC Based Algorithm for Automotive RADARs IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-23 Maria Antonia Maisto, Angela Dell'Aversano, Adriana Brancaccio, Ivan Russo, Raffaele Solimene
In this paper, a computationally light single-snapshot multiple signal classification (MUSIC) algorithm is presented for multidimensional estimation in the framework of automotive radar systems. In particular, for the sake of simplicity, we focus on the two-dimensional (2D) range-angle processing. The goal is to reduce the computational effort with respect to the standard 2D MUSIC implementation and
-
Disparity Computation With Low Intensity Quantization on Stereo Image Pairs IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-19 Huei-Yung Lin, Tsai-Yu Hsu
From variate bit-rate stereo matching, it is observed that the image pair with a low intensity quantization level is still capable of providing good disparity maps. In this article, a mathematical model representing the level of disparity discontinuity is proposed to formulate the mismatching prediction based on the intensity quantization. It is used to derive the minimum quantization level for quality-assured
-
Joint Distributed Traveltime and Full Waveform Tomography for Enhanced Subsurface Imaging in Seismic Networks IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-16 Ban-Sok Shin, Dmitriy Shutin
-
Fractional Optimization With the Learnable Prior for Electrical Capacitance Tomography IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-15 Jing Lei, Qibin Liu
Despite the impressive potential of electrical capacitance tomography technology in the process industry, its application is hampered by low-quality images. To unleash its potential, we propose a new fractional optimization model with the semi-supervised learning prior (SSLP) for imaging, which synergizes the physical mechanism of measurement with semi-supervised learning. An efficient optimizer is
-
A Study on Establishing a Dynamic Color Schlieren System to Observe Airflow and Predict Temperature Changes IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-15 Bo-Lin Jian, Jia-Ming Zhou
Color schlieren technique visualizes invisible fluid waves (e.g., airflows, sound waves) by utilizing color filters for airflow temperature evaluation through dynamic color distribution, offering detailed analysis advantages over infrared thermal imaging. This study adopts low-cost, transparent projector films as color filters, enhancing intuitive perception but introducing haze due to laser printing
-
Learn Stable MRI Under-Sampling Pattern With Decoupled Sampling Preference IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-02 Haoze Sun, Chenyu Tian, Jing Xiao, Yujiu Yang
Accelerating the scanning time of magnetic resonance imaging (MRI) and improving the imaging quality is critical to the patient experience in clinical applications. In accelerated MRI, MR data can be under-sampled in the raw k-space, and the available measurements are used for image reconstruction. Many recent studies have shown that the quality of image reconstruction depends largely on the MRI under-sampling
-
Interferometric Lensless Imaging: Rank-One Projections of Image Frequencies With Speckle Illuminations IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-02 Olivier Leblanc, Matthias Hofer, Siddharth Sivankutty, Hervé Rigneault, Laurent Jacques
Lenslessillumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an interferometric matrix—a matrix encoding the spectral content of the sample image
-
ADMMNet-Based Deep Unrolling Method for Ghost Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-02 Yuchen He, Yue Zhou, Jianming Yu, Hui Chen, Huaibin Zheng, Jianbin Liu, Yu Zhou, Zhuo Xu
Due to the advantages that different from traditional imaging methods, ghost imaging (GI) attracts more and more researchers' attention, which has the potential applications in the fields of lidar, non-field-of-view imaging, etc. On the other hand, GI has been suffering from poor imaging quality and high sampling rate. In recent years, compressed sensing (CS)-based and deep learning (DL)-based methods
-
The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-02-01 Yunsong Liu, Congyu Liao, Kawin Setsompop, Justin P. Haldar
In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective
-
Sign-Coded Exposure Sensing for Noise-Robust High-Speed Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-29 R. Wes Baldwin, Vijayan Asari, Keigo Hirakawa
We present a novel Fourier camera, an in-hardware optical compression of high-speed frames employing pixel-level sign-coded exposure where pixel intensities temporally modulated as positive and negative exposure are combined to yield Hadamard coefficients. The orthogonality of Walsh functions ensures that the noise is not amplified during high-speed frame reconstruction, making it a much more attractive
-
LIR-Net:Learnable Iterative Reconstruction Network for Fan Beam CT Sparse-View Reconstruction IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-25 Yubin Cheng, Qing Li, Runrui Li, Tao Wang, Juanjuan Zhao, Qiang Yan, Zia Ur Rehman, Long Wang, Yan Geng
In computed tomography (CT), although sparse sampling of projections effectively mitigates radiation problems, the quality of CT images is severely compromised. Recovering high-quality CT images from sparsely sampled data is a challenging task. Recently, “Iterative Theory + Deep Learning” schemes have shown promising results in CT reconstruction tasks. In this paper, we propose an Iterative Reconstruction
-
Densely Connected Convolutional Neural Network-Based Invalid Data Compensation for Brain Electrical Impedance Tomography IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-24 Yanyan Shi, Yajun Lou, Meng Wang, Ke Yang, Zhen Gao, Feng Fu
Electrical impedance tomography (EIT) is a potential technique for the brain imaging. With this technique, pathology related conductivity variation inside the intracerebral domain can be visualized. However, electrode disconnection is a common phenomenon during the long-term monitoring with EIT. This will cause data loss and lead to image reconstruction failure. To address this problem, a novel method
-
Holographic Reconstruction With All-Acoustic Diffractive Network IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-24 Wenting Gu, Jialong Wang, Shouyu Chai, Tho N.H.T. Tran, Dean Ta, Xin Liu
Acoustic hologram has shown its great potential for controlling complex pressure fields with minimal hardware, making it attractive for various applications in particle manipulation, cellular assembly, and ultrasound therapy, etc. However, conventional inline reconstruction methods suffer from twin-image artifacts. Computer-based iterative retrieval or deep learning methods have been used to eliminate
-
Spatially Varying Exposure With 2-by-2 Multiplexing: Optimality and Universality IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-22 Xiangyu Qu, Yiheng Chi, Stanley H. Chan
The advancement of new digital image sensors has enabled the design of exposure multiplexing schemes where a single image capture can have multiple exposures and conversion gains in an interlaced format, similar to that of a Bayer color filter array. In this article, we ask the question of how to design such multiplexing schemes for adaptive high-dynamic range (HDR) imaging where the multiplexing scheme
-
Spatial-Frequency Multi-Scale Transformer for Deblurring and Shape-Preserving Reconstruction in Magnetic Particle Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-22 Yaxin Shang, Jie Liu, Yanjun Liu, Yueqi Wang, Yusong Shen, Xiangjun Wu, Liwen Zhang, Hui Hui, Jie Tian
Magnetic particle imaging (MPI) is a novel and emerging functional imaging technique that visualizes the spatial distribution of magnetic nanoparticles (MNPs). While the X-space method considers some important physical properties of MPI systems, it also neglects some phenomena, such as signals generated by MNPs outside (but close-to) the field-free region. Therefore, the X-space approach often results
-
Compact Representation of Light Field Data for Refocusing and Focal Stack Reconstruction Using Depth Adaptive Multi-CNN IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-19 Chun Zhao, Byeungwoo Jeon
A light field camera can record image information of the same scene from different viewpoints. Its 4D data allow post processing among which digital refocusing is popular due to its practical usability. However, in server-device applications, the transmission cost of the large volume of data from a server to a device is a major issue. In this paper, we propose a novel hardware-friendly refocusing representation
-
Joint Denoising and HDR for RAW Image Sequences IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-16 A. Buades, O. Martorell, M. Sánchez-Beeckman
We propose a patch-based method for the simultaneous denoising and fusion of a sequence of multi-exposed RAW images. A spatio-temporal criterion is used to select similar patches along the sequence, and a weighted principal component analysis (WPCA) simultaneously denoises and fuses the multi-exposed data. The overall strategy permits to denoise and fuse the set of images without the need to recover
-
Gates-Controlled Deep Unfolding Network for Image Compressed Sensing IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-16 Tiancheng Li, Qiurong Yan, Quan Zou, Qianling Dai
Deep Unfolding Networks (DUNs) have demonstrated remarkable success in compressed sensing by integrating optimization solvers with deep neural networks. The issue of information loss during the unfolding process has received significant attention. To address this issue, many advanced deep unfolding networks utilize memory mechanisms to augment the information transmission during iterations. However
-
Imaging Through the Atmosphere Using Turbulence Mitigation Transformer IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-16 Xingguang Zhang, Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan
Restoring images distorted by atmospheric turbulence is a ubiquitous problem in long-range imaging applications. While existing deep-learning-based methods have demonstrated promising results in specific testing conditions, they suffer from three limitations: (1) lack of generalization capability from synthetic training data to real turbulence data; (2) failure to scale, hence causing memory and speed
-
Snapshot Compressive Imaging Using Domain-Factorized Deep Video Prior IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-11 Yu-Chun Miao, Xi-Le Zhao, Jian-Li Wang, Xiao Fu, Yao Wang
Snapshot compressive imaging (SCI) aims at efficiently capturing high-dimensional data (e.g., multi-spectral images and videos) using a two-dimensional detector, which is a hardware-friendly data acquisition paradigm. However, because of the complex structure of videos (such as the dynamic background and moving foreground), it is challenging to reconstruct a video from the captured measurement. Existing
-
A Transformer-Based Architecture for High-Resolution Stereo Matching IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-10 Di Jia, Peng Cai, Qian Wang, Ninghua Yang
The Transformer architecture is now widely used due to its superior parallel computing and global modelling capabilities. In this paper, We build a dense F eature E xtraction T ransformer (FET) for stereo matching tasks, incorporating Transformer and convolution blocks. In stereo matching tasks, FET has three advantages: 1) For stereo image pairs with high resolution, Transformer blocks joined with
-
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-09 Luke Lozenski, Hanchen Wang, Fu Li, Mark Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa
Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost
-
A 3-D Ultrasound Tomography Method for Bone Morphology Evaluation IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-04 Qinzhen Shi, Tianhua Zhou, Yuan Liu, Yucheng He, Lingwei Shi, Yifang Li, Dean Ta
Accurately imaging bones using ultrasound has been a long-standing challenge, primarily due to the high attenuation, significant acoustic impedance contrast at cortical boundaries, and the unknown distribution of sound velocity. Furthermore, two-dimensional (2-D) ultrasound bone imaging has limitations in diagnosing osteoporosis from a morphological perspective, as it lacks stereoscopic spatial information
-
Simultaneous Multifrequency Demodulation for Single-Shot Multiple-Path ToF Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2024-01-01 Peyman Fayyaz Shahandashti, Paula López, Víctor Manuel Brea, Daniel García-Lesta, Miguel Heredia Conde
Indirect Time-of-Flight (iToF) sensors measure the received signal's phase shift or time delay to calculate depth. In realistic conditions, however, recovering depth is challenging as reflections from secondary scattering areas or translucent objects may interfere with the direct reflection, resulting in inaccurate 3D estimates. We propose a new measurement concept including a single-shot on-chip multifrequency
-
DER-GAN: Dual-Energy Recovery GAN for Conebeam CT IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-12-29 Jiajun Xiang, Aihua Mao, Jiayi Xie, Hongbin Han, Xianghong Wang, Peng Jin, Jichen Du, Mingchao Ding, Lequan Yu, Tianye Niu
Dual-energy cone-beam computed tomography (DE-CBCT) integrates dual-energy imaging seamlessly into the CBCT system, offering a practical solution for real-time clinical applications in treatment rooms. Traditional DE-CBCT systems often rely on intricate hardware or dual scanning, imposing significant constraints on the broader application of dual-energy CT (DECT) in CBCT machines. In this study, we
-
Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-12-28 Gyutaek Oh, Sukyoung Jung, Jeong Eun Lee, Jong Chul Ye
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning approaches have been studied for motion artifact reduction. Unfortunately, most existing models are trained in a supervised manner, requiring paired motion-corrupted and motion-free images, or are based on a strict
-
Differentiable Uncalibrated Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-12-22 Sidharth Gupta, Konik Kothari, Valentin Debarnot, Ivan Dokmanić
We propose a differentiable imaging framework to address uncertainty in measurement coordinates such as sensor locations and projection angles. We formulate the problem as measurement interpolation at unknown nodes supervised through the forward operator. To solve it we apply implicit neural networks, also known as neural fields, which are naturally differentiable with respect to the input coordinates
-
TRINIDI: Time-of-Flight Resonance Imaging With Neutrons for Isotopic Density Inference IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-12-21 Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman
Accurate reconstruction of 2D and 3D isotope densities is a desired capability with great potential impact in applications such as evaluation and development of next-generation nuclear fuels. Neutron time-of-flight (TOF) resonance imaging offers a potential approach by exploiting the characteristic neutron absorption spectra of each isotope. However, it is a major challenge to compute quantitatively
-
Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-23 Wentao Chao, Xuechun Wang, Yingqian Wang, Guanghui Wang, Fuqing Duan
Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing LF depth estimation methods consider depth estimation as a regression problem, where a pixel-wise L1 loss is employed to supervise the training process. However, the disparity map is only a sub-space projection (i.e., an expectation) of the disparity distribution, which is essential for models to learn. In
-
Region of Interest Selection-Based Autofocusing for High Magnification Systems IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-20 Islam Helmy, Wooyeol Choi
The selection of the region of interest (ROI) in an image for measuring the focus level of high-magnification astronomical observations is arduous. Precise focusing is a principal parameter that controls the quality of images for astronomical observations, leading to proper scientific research. Our proposed scheme detects and evaluates four candidate stars using the full width at half maximum (FWHM)
-
Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-20 Xi Chen, Chaoyang Zhang, Ti Bai, Shaojie Chang
Photon counting spectral computed tomography (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting the energy properties of the scanned object. Due to the limited photon numbers of each energy channel and the nonideal detector response, the reconstructed images usually contain considerable noise. With the development of the deep learning (DL) technique, different
-
XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-16 Di Guo, Zhangren Tu, Yi Guo, Yirong Zhou, Jian Wang, Zi Wang, Tianyu Qiu, Min Xiao, Yinran Chen, Liubin Feng, Yuqing Huang, Donghai Lin, Qing Hong, Amir Goldbourt, Meijin Lin, Xiaobo Qu
Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration
-
Vignetting Correction Using an Optical Model and Constant Chromaticity Prior IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-17 Beinan Yu, Jiacheng Ying, Lun Luo, Si-Yuan Cao, Xiansong Bao, Hui-Liang Shen
Vignetting correction is a key pre-processing module for most imaging systems related to computer vision applications. Vignetting estimation is difficult for the current imaging systems due to the complex optical components and challenging vignetting feature extraction. In this work, we propose an algorithm for single-image vignetting correction. We present an optical aperture limit model that uses
-
Maximum Likelihood Based Phase-Retrieval Using Fresnel Propagation Forward Models With Optional Constraints IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-10 K. Aditya Mohan, Jean-Baptiste Forien, Venkatesh Sridhar, Jefferson Cuadra, Dilworth Parkinson
X-ray phase-contrast tomography (XPCT) is widely used for high contrast 3D imaging using either synchrotron or laboratory microfocus X-ray sources. XPCT enables an order of magnitude improvement in image contrast of the reconstructed material interfaces with low X-ray absorption contrast. The dominant approaches to 3D reconstruction using XPCT relies on the use of phase-retrieval algorithms that make
-
Discovering Structure From Corruption for Unsupervised Image Reconstruction IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-06 Oscar Leong, Angela F. Gao, He Sun, Katherine L. Bouman
We consider solving ill-posed imaging inverse problems without access to an image prior or ground-truth examples. An overarching challenge in these inverse problems is that an infinite number of images, including many that are implausible, are consistent with the observed measurements. Thus, image priors are required to reduce the space of possible solutions to more desirable reconstructions. However
-
Boosting Light Field Image Super Resolution Learnt From Single-Image Prior IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-06 Xingzheng Wang, Zixuan Wang, Wenhao Huang, Kaiqiang Chen, Lihua Li
In recent years, many deep learning networks are proposed for light field super resolution (LFSR). LFSR problem is essentially ill-posed since unknown detail information need to be predicted. Hence LFSR networks require plentiful content information (e.g., shape, color, texture) learned from sufficiently diverse scenarios. However, due to the high collection cost, existing light field datasets are
-
Spectral2Spectral: Image-Spectral Similarity Assisted Deep Spectral CT Reconstruction Without Reference IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-11-06 Xiaodong Guo, Yonghui Li, Dingyue Chang, Peng He, Peng Feng, Hengyong Yu, Weiwen Wu
Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction
-
Projected Multi-Agent Consensus Equilibrium (PMACE) With Application to Ptychography IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-30 Qiuchen Zhai, Gregery T. Buzzard, Kevin Mertes, Brendt Wohlberg, Charles A. Bouman
Multi-Agent Consensus Equilibrium (MACE) formulates an inverse imaging problem as a balance among multiple update agents such as data-fitting terms and denoisers. However, each such agent operates on a separate copy of the full image, leading to redundant memory use and slow convergence when each agent affects only a small subset of the full image. In this article, we extend MACE to Projected Multi-Agent
-
MD-GraphFormer: A Model-Driven Graph Transformer for Fast Multi-Contrast MR Imaging IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-30 Jiazhen Wang, Yan Yang, Heran Yang, Chunfeng Lian, Zongben Xu, Jian Sun
In magnetic resonance imaging (MRI), multi-contrast pulse sequences are routinely acquired, providing complementary information for medical diagnosis. Compared with the single-contrast MR image reconstruction, the multi-contrast MR imaging could further accelerate data acquisition and improve reconstruction quality by leveraging the complementary information of multi-contrast MR images. In this paper
-
Bistatic Forward-Looking SAR Imaging of Uniformly Moving Target Based on Improved BP Algorithm IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-30 Guangzhao Qian, Yong Wang, Boya Zhang, Mingfan Liu
Back projection (BP) algorithm is a common imaging algorithm that processes signals in the time domain. It can be used to create a clear image of a stationary target in the bistatic forward-looking synthetic aperture radar (BFSAR) system. However, when a target has a translational velocity or a complex rotation, the motion of the target causes additional range migration and time delay in the echo data
-
Model-Based Reconstruction for Multi-Frequency Collimated Beam Ultrasound Systems IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-16 Abdulrahman M. Alanazi, Singanallur Venkatakrishnan, Hector Santos-Villalobos, Gregery T. Buzzard, Charles Bouman
Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonly used reconstruction algorithms such as
-
Synthetic Aperture Imaging of Dispersive Targets IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-19 Arnold D. Kim, Chrysoula Tsogka
We introduce a dispersive point target model based on scattering by a particle in the far-field. The synthetic aperture imaging problem is then expanded to identify these targets and recover their locations and frequency dependent reflectivities. We show that Kirchhoff migration (KM) is able to identify dispersive point targets in an imaging region. However, KM predicts target locations that are shifted
-
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-16 Ricardo Augusto Borsoi, Deniz Erdoğmuş, Tales Imbiriba
Although considerable effort has been dedicated to improving the solution to the hyperspectral unmixing problem, non-idealities such as complex radiation scattering and endmember variability negatively impact the performance of most existing algorithms and can be very challenging to address. Recently, deep learning-based frameworks have been explored for hyperspectral umixing due to their flexibility
-
Time-Resolved Reconstruction of Motion, Force, and Stiffness Using Spectro-Dynamic MRI IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-16 Max H. C. van Riel, Tristan van Leeuwen, Cornelis A. T. van den Berg, Alessandro Sbrizzi
Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-space data. This work presents an extended
-
Low-Light Image Enhancement via Implicit Priors Regularized Illumination Optimization IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-11 Qianting Ma, Yang Wang, Tieyong Zeng
Low-light image enhancement is a very challenging problem due to insufficient or uneven illumination, complicated noise and low contrast. Retinex-based methods have shown to be effective in separating the illumination from the reflectance with well-designed priors. However, the commonly used hand-crafted priors may not model the piecewise smoothness of the illumination. In this article, we propose
-
Local-Global Dynamic Filtering Network for Video Super-Resolution IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-04 Chaopeng Zhang, Xingtao Wang, Ruiqin Xiong, Xiaopeng Fan, Debin Zhao
Video super-resolution (VSR) has been greatly advanced by the use of deep learning techniques, but the challenge of handling motion variability has remained a bottleneck. Many previous methods have treated motions equally, leading to suboptimal alignment. In this article, we propose a Local-Global Dynamic Filtering Network (LGDFNet) to address this issue. LGDFNet uses a divide-and-conquer strategy
-
Blind Motion Deblurring With Pixel-Wise Kernel Estimation via Kernel Prediction Networks IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-04 Guillermo Carbajal, Patricia Vitoria, José Lezama, Pablo Musé
In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. For this reason, approaches that explicitly use a forward degradation model received significantly less attention. However, a well-defined specification of the blur genesis, as an intermediate step, promotes the generalization
-
Far-Field Acoustic Imaging Enhancement Using Compact Graded Refractive Index Metamaterials IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-10-04 Zan Li, Jinyu Ma, Jian Li, Xinjing Huang
Acoustic metamaterials (AMMs) are promising in high-performance acoustic detection and imaging; however, few can be combined with sensors to form a practical detector, and most imaging attempts use under near-field conditions. This article provides a systematic method of using a compact graded refractive index (GRIN) AMM with an embedded microelectromechanical systems (MEMS) microphone to enhance far-field
-
Extended Depth-of-Field Lensless Imaging Using an Optimized Radial Mask IEEE Trans. Comput. Imaging (IF 5.4) Pub Date : 2023-09-29 José Reinaldo Cunha Santos A. V. Silva Neto, Tomoya Nakamura, Yasushi Makihara, Yasushi Yagi
The freedom of design of coded masks used by mask-based lensless cameras is an advantage these systems have when compared to lens-based ones. We leverage this freedom of design to propose a shape-preserving optimization scheme for a radial-type amplitude coded mask. Due to the depth-independency of the radial mask's point spread function, they can be used for extending the effective depth of field