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Elevational Synthetic Aperture Focusing for Rotated Array-Based Three-dimensional Ultrasound Imaging

IEEE ACCESS 2025年 13卷 45458-45467页

作者： Murakami, Ryo Wang, Yang Tang, Yichuan Tsumura, Ryosuke Fischer, Gregory S. Zhang, Haichong K. Worcester Polytech Inst Dept Robot Engn Worcester MA 01609 USA Worcester Polytech Inst Dept Biomed Engn Worcester MA 01609 USA Worcester Polytech Inst Dept Comp Sci Engn Worcester MA 01609 USA

Three-dimensional (3d) ultrasound (US) imaging is widely used for real-time, non-ionizing, and cost-effective medical diagnostics. However, using a one-dimensional (1d) transducer often results in limited elevational resolution due to the inherent beam thickness. In this paper, we introduce an elevational Synthetic Aperture Focusing (SAF) algorithm specifically designed for rotational 3d US imaging. Unlike previous methods requiring channel data, our approach operates on in-plane beamformed radio-frequency (RF) data, making it more accessible on many commercial scanners. Through simulations and experiments, we demonstrate significant improvements in elevational resolution (up to 96.4%) and contrast (up to 274.7%). These findings highlight the potential of the proposed algorithm to enhance both research and clinical applications of rotational 3d US imaging.

关键词： Imaging Three-dimensional displays Transducers Apertures image resolution Ultrasonic imaging image quality Focusing Probes Radio frequency Biomedical imaging focusing image processing medical diagnostic imaging ultrasonics imaging

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High Accurate and Efficient 3d Network for image Reconstruction of diffractive-Based Computational Spectral Imaging

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IEEE ACCESS 2024年 12卷 120720-120728页

作者： Fan, Hao Li, Chenxi Xu, Huangrong Zhao, Lvrong Zhang, Xuming Jiang, Heng Yu, Weixing Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol Xian 710119 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Hong Kong Polytech Univ Dept Appl Phys Hong Kong Peoples R China

diffractive optical imaging spectroscopy as a promising miniaturized and high throughput portable spectral imaging technique suffers from the problem of low precision and slow speed, which limits its wide use in various applications. To reconstruct the diffractive spectral image more accurately and fast, a three-dimensional spectrum recovery algorithm is proposed in this paper. The algorithm takes advantage of a neural network for image reconstruction which consists of a U-Net architecture with 3d convolutional layers to improve the processing precision and speed. Numerical experiments are conducted to prove its effectiveness. It is shown that the mean peak signal-to-noise ratio (MPSNR) of the recovered image relative to the original image is improved by 1.8 dB in comparison to other traditional methods. In addition, the obtained mean structural similarity (MSSIM) of 0.91 meets the standard of discrimination to human eyes. Moreover, the algorithm runs in just 0.36 s, which is faster than other traditional methods. 3d convolutional networks play a critical role in performance improvement. Improvements in processing speed and accuracy have greatly benefited the realization and application of diffractive optical imaging spectroscopy. The new algorithm with high accuracy and fast speed has a great potential application in diffraction lens spectroscopy and paves a new way for emerging more portable spectral imaging technique.

关键词： Imaging Lenses image reconstruction Three-dimensional displays Accuracy Optical imaging Spectral analysis Inverse problems diffraction Computational imaging spectral imaging inverse problems diffractive lenses

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Robust 3d Semantic Segmentation Based on Multi-Phase Multi-Modal Fusion for Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1602-1614页

作者： Ni, Peizhou Li, Xu Xu, Wang Kong, dong Hu, Yue Wei, Kun Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China

3d semantic segmentation is a key technology for intelligent vehicles. Recently, great efforts have been made to achieve accurate and robust 3d semantic segmentation results through LidAR-camera fusion, due to the complementary information of images like color and texture. However, current LidAR-camera fusion methods still lag behind LidAR-only methods in complex and varied scenarios containing sparse and similar geometric features. Three main factors limit their performance: sparse mappings between two modalities, the contribution of different modality features, and the gap between results of two modalities. Addressing that, we present a multi-phase LidAR-camera fusion method, which comprehensively fuses information of two modalities in three phases: first, a context sampler block is proposed to fuse low-level image contextual features into LidAR points;second, a cross dimensional fusion block is designed to implement the interaction of shared features for hierarchical supervision while retaining private features for jointly refining their predictions;third, a modality perceptual conformance loss is induced to make them learn from each other in an explicit manner by minimizing the gap between two modality predictions. Quantitative and qualitative evaluations with state-of-the-art (SOTA) methods are performed on urban dataset SemanticKITTI, off-road dataset RELLIS-3d and our unstructured test set. The experimental results validate the competitiveness of our method in improving accuracy and scene-adaptivity.

关键词： Three-dimensional displays Semantic segmentation Point cloud compression Laser radar Fuses Intelligent vehicles Semantics Intelligent vehicles 3d semantic segmentation multi-modal fusion multi-phase

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Variational autoencoders for 3d data processing

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期57卷 42-42页

作者： Molnar, Szilard Tamas, Levente Tech Univ Cluj Napoca Automat Dept Memorandumului St 25 Cluj Napoca 400114 Romania

Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the information with the possibility to decode and generalize new samples. This capability was heavily explored for 2d image processing;however, only limited research focuses on VAEs for 3d data processing. In this article, we provide a thorough review of the latest achievements in 3d data processing using VAEs. These 3d data types are mostly point clouds, meshes, and voxel grids, which are the focus of a wide range of applications, especially in robotics. First, we shortly present the basic autoencoder with the extensions towards the VAE with further subcategories relevant to discrete point cloud processing. Then, the 3d data specific VAEs are presented according to how they operate on spatial data. Finally, a few comprehensive table summarizing the methods, codes, and datasets as well as a citation map is presented for a better understanding of the VAEs applied to 3d data. The structure of the analyzed papers follows a taxonomy, which differentiates the algorithms according to their primary data types and application domains.

关键词： Variational autoencoder Survey 3d data

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Quantitative real-time phase microscopy for extended depth-of-field imaging based on the 3d single-shot differential phase contrast (ssdPC) imaging method

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OPTICS EXPRESS 2024年第2期32卷 2081-2096页

作者： Wang, Jingfan Zhao, Xing Wang, Yan Li, da Nankai Univ Inst Modern Opt Tianjin Key Lab Microscale Opt Informat Sci & Tec Tianjin 300350 Peoples R China Nankai Univ Eye Hosp Eye Inst Tianjin Peoples R China Tianjin Eye Hosp Tianjin Eye Inst Tianjin Key Lab Ophthalmol & Visual Sci Tianjin 300020 Peoples R China

Optical diffraction tomography (OdT) is a promising label-free imaging method capable of quantitatively measuring the three-dimensional (3d) refractive index distribution of transparent samples. In recent years, partially coherent OdT (PC-OdT) has attracted increasing attention due to its system simplicity and absence of laser speckle noise. Quantitative phase imaging (QPI) technologies represented by Fourier ptychographic microscopy (FPM), differential phase contrast (dPC) imaging and intensity diffraction tomography (IdT) need to collect several or hundreds of intensity images, which usually introduce motion artifacts when shooting fast-moving targets, leading to a decrease in image quality. Hence, a quantitative real-time phase microscopy (qRPM) for extended depth of field (dOF) imaging based on 3d single-shot differential phase contrast (ssdPC) imaging method is proposed in this research study. qRPM incorporates a microlens array (MLA) to simultaneously collect spatial information and angular information. In subsequent optical information processing, a deconvolution method is used to obtain intensity stacks under different illumination angles in a raw light field image. Importing the obtained intensity stack into the 3d dPC imaging model is able to finally obtain the 3d refractive index distribution. The captured four-dimensional light field information enables the reconstruction of 3d information in a single snapshot and extending the dOF of qRPM. The imaging capability of the proposed qRPM system is experimental verified on different samples, achieve single-exposure 3d label-free imaging with an extended dOF for 160 mu m which is nearly 30 times higher than the traditional microscope system.

关键词： diffractive optical elements image quality Imaging techniques information processing Phase contrast Plenoptic imaging

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dehazeGS: 3d Gaussian Splatting for Multi-image Haze Removal

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IEEE SIGNAL processing LETTERS 2025年 32卷 736-740页

作者： Ma, Chenjun Zhao, Jieyu Chen, Jian Ningbo Univ Dept Comp Sci & Technol Ningbo 315211 Peoples R China

Neural Radiance Fields (NeRF) have advanced 3d reconstruction by learning implicit representations of scenes from multi-view images, yet their effectiveness is limited in environments with scattering medium. Existing methods that incorporate scattering models into NeRF frameworks face issues with slow training speeds and high memory demands. This paper presents dehazeGS, a novel haze removal and reconstruction method based on 3d Gaussian Splatting (3dGS). Our approach integrates the Koschmieder scattering model into the 3dGS framework, enabling effective separation of objects and scattering medium. This method leverages a point-based representation to achieve high-quality scene reconstruction while significantly reducing computational and memory overhead. Experimental results on both synthetic and real datasets demonstrate that our method outperforms existing approaches in terms of dehazing quality and reconstruction performance, effectively synthesizing clear images from foggy scenes. Our findings suggest that integrating scattering models with 3dGS offers a promising solution for applications in adverse weather conditions.

关键词： Three-dimensional displays Scattering image color analysis Neural radiance field Rendering (computer graphics) Covariance matrices Atmospheric modeling image reconstruction Computational modeling Point cloud compression 3d Gaussian splatting image dehazing neural radiance field novel view synthesis

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Towards 3d Colored Mesh Saliency: database and Benchmarks

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IEEE TRANSACTIONS ON MULTIMEdIA 2024年 26卷 3580-3591页

作者： ding, Xiaoying Chen, Zhao Lin, Weisi Chen, Zhenzhong Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

While saliency detection for 3d meshes has been extensively studied in the past decades, only a little work considers color information, and most of existing 3d mesh saliency databases are collected using meshes without color information. The lack of publicly available 3d colored mesh saliency database hinders the research progress in 3d colored mesh saliency detection. In this article, we established a novel 3d colored mesh saliency database (3dCMS) based on an eye-tracking experiment and investigated subjects' visual attention behavior towards 3d colored meshes. Based on the investigations, a novel 3d colored mesh saliency detection framework is proposed which takes both color and geometric features into consideration. To evaluate the performance of the proposed algorithm, we compare it with several relevant methods and apply it to 3d mesh simplification task. The quantitative and qualitative evaluation results demonstrate the superior performance of the proposed framework. The proposed 3dCMS database will be made publicly available.(1)

关键词： Three-dimensional displays Saliency detection databases Visualization image color analysis Gaze tracking Feature extraction Mesh saliency detection visual attention 3d colored mesh mesh simplification

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Frame-Padded Multiscale Transformer for Monocular 3d Human Pose Estimation

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IEEE TRANSACTIONS ON MULTIMEdIA 2024年 26卷 6191-6201页

作者： Zhong, Yuanhong Yang, Guangxia Zhong, daidi Yang, Xun Wang, Shanshan Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Bioengn Coll Chongqing 400044 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China Anhui Univ Inst Phys Sci Hefei 230039 Peoples R China Anhui Univ Inst Informat Technol Hefei 230039 Peoples R China

Monocular 3d human pose estimation is an ill-posed problem in computer vision due to its depth ambiguity. Most existing works supplement the depth information by extracting temporal pose features from video frames, and they have made notable progress. However, these approaches divide a long sequence of video frames into multiple short sequences for separate processing, which leads to the loss of complementary information between sequences. Furthermore, the short-term temporal correlation among frames in a sequence is often not fully exploited. To model temporal dependencies efficiently, we propose the frame-padded multiscale transformer approach, which includes a frame-padded video sequence preprocessing step and a multiscale temporal transformer backbone. Our approach addresses the omission of the temporal features of edge frames in existing approaches by padding video frames in the shallow layer. In addition, we extract the temporal information of 3d human poses using a multiscale transformer to enhance the short-term correlation of human pose skeleton keypoints. Extensive experiments validate the effectiveness of our approach on two popular datasets: Human3.6M and MPI-INF-3dHP. The results show that our approach achieves state-of-the-art performance.

关键词： Transformers Three-dimensional displays Feature extraction Pose estimation Pipelines Correlation image restoration 3d human pose estimation temporal attention transformer frame padding monocular

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Array SAR 3-d Sparse Imaging Based on Regularization by denoising Under Few Observed data

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IEEE TRANSACTIONS ON GEOSCIENCE ANd REMOTE SENSING 2024年 62卷 1页

作者： Wang, Yangyang Zhan, Xu Gao, Jing Yao, Jinjie Wei, Shunjun Bai, Jiansheng North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Array synthetic aperture radar (SAR) 3-d imaging can obtain 3-d information of the target region, which is widely used in environmental monitoring and scattering information measurement. In recent years, with the development of compressed sensing (CS) theory, sparse signal processing is used in array SAR 3-d imaging. Compared with matched filter (MF), sparse SAR imaging can effectively improve image quality. However, sparse imaging based on handcrafted regularization functions suffers from target information loss in few observed SAR data. Therefore, in this article, a general 3-d sparse imaging framework based on regularization by denoising (REd) and proximal gradient descent-type method for array SAR is presented. First, we construct explicit prior terms via state-of-the-art denoising operators instead of regularization functions, which can improve the accuracy of sparse reconstruction and preserve the structure information of the target. Then, different proximal gradient descent-type methods are presented, including a generalized alternating projection (GAP) and an alternating direction method of multiplier (AdMM), which is suitable for high-dimensional data processing. Additionally, the proposed method has robust convergence, which can achieve sparse reconstruction of 3-d SAR in few observed SAR data. Extensive simulations and real data experiments are conducted to analyze the performance of the proposed method. The experimental results show that the proposed method has superior sparse reconstruction performance.

关键词： Imaging Three-dimensional displays Synthetic aperture radar image reconstruction Radar polarimetry Convergence Scattering 3-d imaging compressed sensing (CS) regularization by denoising (REd) synthetic aperture radar (SAR)

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Application of 3d image Reconstruction on Landscape Architecture in Environmental design System

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Computer-Aided design and Applications 2024年第S1期21卷 46-60页

作者： Wang, Jun Niu, Guanxiang Digital Creative Design Institute Henan Polytechnic Henan Zhengzhou450046 China Henan branch of Agricultural Development Bank of China Henan Zhengzhou450000 China

Computer software assistance is widely used in the field of design due to its fast response and convenience in current environmental design. Combining with semantic segmentation technology for efficient planning of three-dimensional landscape environment can effectively improve the visual effect of landscape environment planning and design. The traditional method first calculates the edge location of the environment image layout and denoises the image layout information, but ignores the filtering processing, resulting in incomplete image layout information. Aiming at the above problems, this article constructs a model of environmental image character understanding and semantic expression based on deep learning (dL), and applies it to CAd modeling of environmental design to improve the accuracy of landscape architecture 3d image reconstruction. The research results indicate that this method has good test results in landscape design of 3d images, and has obvious advantages over traditional modeling methods in operational efficiency, which provides an important theoretical basis for the analysis of landscape architecture distribution rationality. The proposed method can effectively improve the integrity of three-dimensional environmental image layout, and provide theoretical and technical basis for the construction of environmental design system. © 2024, CAd Solutions, LLC. All rights reserved.

关键词： Semantics

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