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Interchannel Antenna Pattern Correction for Azimuth Digital Beamforming in Airborne SAR

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Castillo, Juan Pablo Navarro Scheiber, Rolf Jaeger, Marc Moreira, Alberto German Aerosp Ctr DLR Microwaves & Radar Inst D-82234 Wessling Germany

High-resolution wide swath (HRWS) synthetic aperture radar (SAR) systems are normally designed to have identical antenna patterns in each receive channel. Nevertheless, due to different factors, this condition might not be satisfied, resulting in a multichannel radar system with different antenna patterns. This letter studies the impact of these relative antenna differences on the performance of a state-of-the-art azimuth motion-adaptive image reconstruction for a real airborne SAR sensor with multiple azimuth channels on receive. The performance of the reconstruction algorithm is evaluated both when these relative differences are neglected and when they are accounted for in the multichannel reconstruction process. Additionally, the range dependence of the antenna pattern as a function of wavenumber and the use of range block processing to minimize the impact of this dependence are analyzed. The results confirm the relevance of including these additional steps in the reconstruction, extending the understanding of SAR systems using digital beamforming (DBF) in azimuth.

关键词： Antennas Azimuth Synthetic aperture radar reconstruction algorithms Receiving antennas Image reconstruction Antenna radiation patterns Filters Doppler effect Spaceborne radar Airborne synthetic aperture radar (SAR) antenna pattern azimuth SAR image reconstruction digital beamforming (DBF) range block processing

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Iterative Approach to Reconstructing Neural Disparity Fields From Light-Field Data

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2025年 11卷 410-420页

作者： Shi, Ligen Liu, Chang Zhao, Xing Qiu, Jun Capital Normal Univ Sch Math Sci Beijing 100048 Peoples R China Beijing Informat Sci & Technol Univ Coll Comp Sci Beijing 100101 Peoples R China

This study proposes a neural disparity field (NDF) that establishes an implicit, continuous representation of scene disparity based on a neural field and an iterative approach to address the inverse problem of NDF reconstruction from light-field (LF) data. NDF enables seamless and precise characterization of disparity variations in three-dimensional scenes and can discretize disparity at any arbitrary resolution, overcoming the limitations of traditional disparity maps that are prone to sampling errors and interpolation inaccuracies. The proposed NDF network architecture utilizes hash encoding combined with multilayer perceptrons (MLPs) to capture detailed disparities in texture levels, thereby enhancing its ability to represent the geometric information of complex scenes. By leveraging the spatial-angular consistency inherent in the LF data, a differentiable forward model to generate a central view image from the LF data is developed. Based on the forward model, an optimization scheme for the inverse problem of NDF reconstruction using differentiable propagation operators is established. Furthermore, an iterative solution method is adopted to reconstruct the NDF in the optimization scheme, which does not require training datasets and applies to LF data captured by various acquisition methods. Experimental results demonstrate that the proposed method can reconstruct high-quality NDF from LF data. The high-resolution disparity can be effectively recovered by NDF, demonstrating its capability for the implicit, continuous representation of scene disparities.

关键词： Image reconstruction Imaging Depth measurement Estimation Iterative methods Inverse problems Cameras Training reconstruction algorithms Image resolution Neural disparity field disparity estimation light-field

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

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2025年 11卷 377-388页

作者： He, Li Chen, Ruitao Liu, Xiangyu Cao, Xu Zhu, Shouping Wang, Yihan Xidian Univ Sch Life Sci & Technol Engn Res Ctr Mol & NeuroImaging Minist Educ Xian 710126 Peoples R China Xidian Univ Xian Key Lab Intelligent Sensing & Regulat Transsc Guangzhou 510555 Peoples R China Xidian Univ Int Joint Res Ctr Adv Med Imaging & Intelligent Di Guangzhou 510555 Peoples R China Xidian Univ Guangzhou Inst Technol Innovat Ctr Adv Med Imaging & Intelligent Med Guangzhou 510555 Peoples R China

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.

关键词： Visualization Convolution Computational modeling Semantics reconstruction algorithms Transformers Feature extraction Image restoration Image reconstruction Faces Deep learning image restoration photoacoustic tomography reconstruction algorithms sparse-view

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Coprime reconstruction of Super-Nyquist Periodic Signal and Sampling Moire Effect

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Tang, Hsien-Chung Shieh, Yueh-Tsung Roy, Rohit Jeng, Shyr-Long Chieng, Wei-Hua Natl Yang Ming Chiao Tung Univ Mech Dept Hsinchu 300093 Taiwan Lunghwa Univ Sci & Technol Mech Dept Taoyuan Taiwan

Sampling moire effect is well-known in signal processing. When a continuous periodic signal x(t) is sampled using a sampling rate fs that does not respect the Nyquist condition, and the signal-frequency fx folds over and gives a special sequence in the sampled signal. The coprime reconstruction ismade possible when the periodic signal frequency fx and the sampling frequency fs satisfy fs/fx=N/M with integer M, N, where N/M is a reduced fraction. This article shows how to rearrange the N sample data into one entire period of the signal using the M periods of the signal. The reconstruction is equivalent to the decryption of the sample data using the method referred to the mathematic operator "multiplicative inverse modulo." In the implementation to verify the coprime reconstruction, we used a commodity DSP board with 4.2 MHz sampling rate to reconstruct the 13.56 MHz signal acquired from the ADC circuit. The signal reconstruction result is comparable to the sampling result for the same signal sampled at 475 MHz sampling rate. The other simulation on the 234 kHz signal under 1 MHz sampling rate yields a very detail information on the signal with a finding that the multiplicative inverse modulo may also be used as the encryption of signal for network security use.

关键词： Signal resolution Resonant frequency reconstruction algorithms Zero current switching Time-frequency analysis Linear systems Sensors Pulse width modulation Frequency modulation Costs Moir & eacute effects coprime Nyquist theorem reconstruction sampling

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PrideDiff: Physics-Regularized Generalized Diffusion Model for CT reconstruction

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IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2025年第2期9卷 157-168页

作者： Lu, Zexin Gao, Qi Wang, Tao Yang, Ziyuan Wang, Zhiwen Yu, Hui Chen, Hu Zhou, Jiliu Shan, Hongming Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Key Lab Data Protect & Intelligent Management Minist Educ Chengdu 610207 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China Sichuan Univ Key Lab Data Protect & Intelligent Management Minist Educ Chengdu 610207 Peoples R China

Achieving a lower radiation dose and a faster imaging speed is a pivotal objective of computed tomography (CT) reconstruction. However, these often come at the cost of compromised reconstruction quality. With the advent of deep learning, numerous CT reconstruction methods rooted in this field have significantly improved the reconstruction performance. Recently, diffusion models have further enhanced training stability and imaging quality for CT. However, many of these methods only focus on CT image domain features, ignoring the intrinsic physical information of the imaging process. Although compressive sensing-based iterative reconstruction algorithms utilize physical prior information, their intricate iterative process poses challenges in training, subsequently influencing their efficiency. Motivated by these observations, we introduce a novel physics-regularized generalized diffusion model for CT reconstruction (PrideDiff). On the one hand, our method further improves the quality of reconstructed images by fusing physics-regularized iterative reconstruction methods with diffusion models. On the other hand, we propose a prior extraction module embedded with temporal features, which effectively improves the performance of the iteration process. Extensive experimental results demonstrate that PrideDiff outperforms several state-of-the-art methods in low-dose and sparse-view CT reconstruction tasks on different datasets, with faster reconstruction speed. We further discuss the effectiveness of relevant components in PrideDiff and validate the stability of the iterative reconstruction process, followed by detailed analysis of computational cost and inference time.

关键词： Image reconstruction Computed tomography Diffusion models Training Noise Feature extraction reconstruction algorithms Plasmas Image restoration Diffusion processes Deep learning (DL) diffusion models iterative reconstruction low-dose computed tomography (CT) sparse-view CT

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OPTIMAL DATA REPLICATION - A NEW APPROACH TO OPTIMIZING PARALLEL EM algorithms ON A MESH-CONNECTED MULTIPROCESSOR FOR 3D PET IMAGE-reconstruction

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1995年第4期42卷 1235-1245页

作者： CHEN, CM LEE, SY AUBURN UNIV DEPT ELECT ENGNAUBURNAL 36849

The EM algorithm promises an estimated image with the maximal likelihood for 3D PET image reconstruction. However, due to its long computation time, the EM algorithm has not been widely used in practice. While several parallel implementations of the EM algorithm have been developed to make the EM algorithm feasible, they do not guarantee an optimal parallelization efficiency. In this paper, we propose a new parallel EM algorithm which maximizes the performance by optimizing data replication on a mesh-connected message-passing multiprocessor. To optimize data replication, we have formally derived the optimal allocation of shared data, group sizes, integration and broadcasting of replicated data as well as the scheduling of shared data accesses. The proposed parallel EM algorithm has been implemented on an iPSC/860 with 16 PEs. The experimental and theoretical results, which are consistent with each other, have shown that the proposed parallel EM algorithm could improve performance substantially over those using unoptimized data replication.

关键词： Positron emission tomography Image reconstruction reconstruction algorithms Iterative algorithms Image converters Biomedical engineering Biomedical computing Broadcasting Data visualization Radioactive materials

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Spk2ImgMamba: Spiking Camera Image reconstruction with Multi-Scale State Space Models

Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jiaoyang Yin Bin Fan Chao Xu Tiejun Huang Boxin Shi State Key Lab of Multimedia Info. Processing School of Computer Science Peking University Nat'l Eng. Research Ctr. of Visual Technology School of Computer Science Peking University Nat'l Key Lab of General AI School of Intelligence Science and Technology Peking University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As a bio-inspired vision sensor, the spiking camera has showcased remarkable capability in high-speed imaging with a sampling rate of 40,000 Hz. Reconstructing clear images from continuous spike streams, which is obtained by each photosensor continuously detecting photons and firing them asynchronously, has garnered significant attention. Despite promising results, existing spike-to-image reconstruction methods face challenges in balancing global receptive fields and efficient computation due to the inherent limitations of their backbones. Recently, due to powerful long-range modeling and linear complexity, the state space model (SSM) has emerged as a competitive alternative to CNNs and Transformers. In this paper, we propose a lightweight spike-to-image reconstruction network that harnesses Mamba as the backbone. Our approach sequentially executes three core modules: temporal information integration, spatial feature enhancement, and progressive image reconstruction. The former accumulates cues across diverse temporal windows to explore both long-term and short-term contexts. Subsequently, to model global dependencies while heightening local detail perception, we develop a multi-scale SSM block characterized by multi-scale multi-direction scanning, which effectively boosts spatial feature representations. Finally, intensity images are decoded progressively from the enhanced light-intensity features. Extensive experiments on both synthetic and real-captured data demonstrate that our approach achieves state-of-the-art performance, with only 10% of the network parameters and nearly two orders of magnitude less computational effort. The code will be available at https://***/interstellarH/Spk2ImgMamba.

关键词： Correlation Computational modeling reconstruction algorithms Vision sensors Transformer cores Streaming media Cameras Transformers Image reconstruction Streams

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SnS₂/WSe₂ van der Waals Single-Detector Spectrometer With a Dynamically Selecting Spectral reconstruction Strategy

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 801-804页

作者： Yang Zhou Haoran Mu Congwen Zhang Renjing Xu Guangyu Zhang Shenghuang Lin Songshan Lake Materials Laboratory Dongguan China MOE Key Laboratory of Laser Life Science Guangdong Provincial Key Laboratory of Laser Life Science and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes College of Biophotonics South China Normal University Guangzhou China Microelectronics Thrust The Hong Kong University of Science and Technology Guangzhou China Institute of Physics Chinese Academy of Science Beijing China

The single-detector spectrometers based on 2D layer van der Waals (vdW) heterojunctions offer advantages in spectral reconstruction due to their high sensitivity, tunable optical properties, and the ability to cover a broad spectral range. There exist two principal algorithms dominating spectrum reconstruction for this kind spectrometer: the Tikhonov regularization method combined with the Least Squares Method (LSM) and neural network-based approaches, particularly Deep Learning (DL). However, both of the algorithms exhibit inherent limitations in spectral reconstruction, which constrain the versatility of computational spectrometers that rely solely on a single algorithm for reconstructing diverse spectral profiles. To overcome this limitation, we introduce an artificial neural network (ANN)-based classification model capable of dynamically selecting the optimal algorithm throughout the reconstruction process. This enables highly accurate spectral reconstruction within the 440-700 nm wavelength range, achieving a spectral resolution of 6 nm. By harnessing the complementary strengths of multiple algorithms, our approach proposes a novel strategy for combining techniques to enhance the precision of spectral reconstructions, laying the groundwork for more sophisticated methods in the future.

关键词： Image reconstruction Broadband communication Accuracy Lakes Classification algorithms Training Support vector machines reconstruction algorithms Radio frequency Photoconductivity

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algorithms and Analyses for Joint Spectral Image reconstruction in Y-90 Bremsstrahlung SPECT

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第5期39卷 1369-1379页

作者： Chun, Se Young Minh Phuong Nguyen Thanh Quoc Phan Kim, Hanvit Fessler, Jeffrey A. Dewaraja, Yuni K. Ulsan Natl Inst Sci & Technol UNIST Dept Elect Engn EE Ulsan 44919 South Korea Delft Univ Technol Sect Biomed Imaging NL-2628 Delft Netherlands Catholic Univ Amer Dept Biomed Engn Washington DC 20064 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Radiol Ann Arbor MI 48109 USA

Quantitative yttrium-90 (Y-90) SPECT imaging is challenging due to the nature of Y-90, an almost pure beta emitter that is associated with a continuous spectrum of bremsstrahlung photons that have a relatively low yield. This paper proposes joint spectral reconstruction (JSR), a novel bremsstrahlung SPECT reconstruction method that uses multiple narrow acquisition windows with accurate multi-band forward modeling to cover a wide range of the energy spectrum. Theoretical analyses using Fisher information and Monte-Carlo (MC) simulation with a digital phantom show that the proposed JSR model with multiple acquisition windows has better performance in terms of covariance (precision) than previous methods using multi-band forward modeling with a single acquisition window, or using a single-band forward modeling with a single acquisition window. We also propose an energy-window subset (ES) algorithm for JSR to achieve fast empirical convergence and maximum-likelihood based initialization for all reconstruction methods to improve quantification accuracy in early iterations. For both MC simulation with a digital phantom and experimental study with a physical multi-sphere phantom, our proposed JSR-ES, a fast algorithm for JSR with ES, yielded higher recovery coefficients (RCs) on hot spheres over all iterations and sphere sizes than all the other evaluated methods, due to fast empirical convergence. In experimental study, for the smallest hot sphere (diameter 1.6cm), at the 20th iteration the increase in RCs with JSR-ES was 66 and 31% compared with single wide and narrow band forward models, respectively. JSR-ES also yielded lower residual count error (RCE) on a cold sphere over all iterations than other methods for MC simulation with known scatter, but led to greater RCE compared with single narrow band forward model at higher iterations for experimental study when using estimated scatter.

关键词： Image reconstruction Single photon emission computed tomography Photonics Bremsstrahlung reconstruction algorithms Convergence Y-90 SPECT joint spectral reconstruction bremsstrahlung energy-window subset

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Application of transform algorithms to high-resolution image reconstruction in optical diffusion tomography of strongly scattering media

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JOURNAL OF ELECTRONIC IMAGING 2003年第4期12卷 602-612页

作者： Konovalov, AB Lyubimov, VV Kutuzov, II Kravtsenyuk, OV Murzin, AG Mordvinov, GB Soms, LN Yavorskaya, LM Russian Fed Nucl Ctr Inst Tech Phys Snezhinsk 456770 Chelyabinsk Reg Russia Russian Fed Nucl Ctr Inst Tech Phys Snezhinsk 456770 Chelyabinsk Reg Russia Res Inst Laser Phys St Petersburg 199034 Russia

The applicability of transform algorithms generally used in projection-computed tomography is substantiated for the, case of medical optical diffusion tomography (ODT). To reconstruct tissue optical inhomogeneities, a new method based on a concept of an average statistical trajectoty for transfer of light energy [photon average trajectory (PAT)] is proposed. By this method, the inverse problem of ODT is reduced to a solution of an integral equation with integration along a PAT Within the internal zone of the object, well away from the boundaries, PATs tend to a straight line, and standard integral algorithms based on the inverse Radon transform may be used to restore diffuse optical images. To demonstrate the capabilities of the PAT method, a numerical experiment on cross sectional reconstruction of cylindrical strongly scattering objects with low-contrast absorbing inhomogeneities is conducted. To solve the time-domain ODT inverse problem, two filtered backprojection algorithms (of Radon and Vainberg) are used. The reconstruction results are compared with those obtained by a well-known software package for temporal optical absorption and scattering tomography, based on multiple solutions of a diffusion equation. It is shown that in important cases of low-contrast absorbing inhomogeneities, the PAT method using the Vainberg algorithm allows reconstruction of tissue optical inhomogeneities with a 20% gain in spatial resolution. (C) 2003 SPIE and IST.

关键词： Acquisition tracking and pointing reconstruction algorithms Receivers Image restoration Spatial resolution Diffusion Fluctuations and noise Inverse optics Tissue optics Tomography

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