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Color holographic zoom system based on a liquid lens

Chinese Optics Letters 2015年第7期13卷 96-100页

作者：王迪王琼华沈川周昕刘超 School of Electronics and Information Engineering Sichuan University Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui University

In this Letter, we propose a color holographic zoom system based on a liquid lens. We use the spatial multiplexing method to realize color reconstruction. By controlling the focal lengths of the liquid lens and the encoded digital lens on the spatial light modulator panel, we can change the magnification of the reconstructed image very quickly, without mechanical parts and keeping the output plane stationary.

关键词： Color Holography Light modulators Multiplexing

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LDG: Lightweight Deformable 3D Gaussians for Single-View Dynamic Scene Reconstruction

LDG: Lightweight Deformable 3D Gaussians for Single-View Dyn...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Youhong Peng Weixing Xie Jinwen Li Shaoqi Wu Zefeng Wang Bingbing Hu Junfeng Yao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Ministry of Culture and Tourism Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent deformable 3D Gaussians methods achieve high-quality reconstruction and real-time rendering. However, they require multi-view information and are not applicable to single-view dynamic scenes captured from mobile phones. Additionally, the high-dimensional hidden layer of deformation MLP and the excessive number of Gaussian primitives and attributes impose significant storage pressure, greatly limiting their practical application. To address the issues, we propose a novel Lightweight Deformable 3D Gaussians teacher-student framework. Specifically, we initialize Gaussian primitives with an initialization strategy designed for single-view scenes, and then optimize the teacher model using color and depth information. For the trained teacher model, we distill deformation MLP, prune Gaussian primitives and Gaussian attributes, and finally obtain a student model with low storage and high efficiency. Public benchmark experiments demonstrate the effectiveness of our framework, showing a compression rate exceeding 4× while maintaining satisfactory rendering quality. Project page: https://***/ldg/.

关键词： Deformable models Training Three-dimensional displays Deformation Memory management Color Rendering (computer graphics) Real-time systems Mobile handsets Speech processing

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Fast Simulations of Electromagnetic Scattering from 1-D Rough Surface through Adaptively Modified Characteristic Basis Function Method 7

Fast Simulations of Electromagnetic Scattering from 1-D Roug...

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2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies

作者： Jing Jing Wang An Qi Wang Zhi Xiang Huang Key Laboratory of intelligent Computing & Signal Processing Anhui University

ISBN: (纸本)9781509059300;9781509059294

In this paper,the Adaptively Modified Characteristic Basis Function Method(AMCBFM) is proposed to fast simulate the electromagnetic scattering from one-dimensional rough ***,the Primary Characteristic Basis Functions(PCBFs) arising from the self-interaction within the self-block are generated,and the coefficients of the basis function and the first current of each block are ***,the Secondary Characteristic Basis Functions(SCBFs) which account for the mutual coupling effects from the other distinct domains are obtained by using the *** higher SCBFs can be also derived through the same ***,a new precision method is applied to control the current error,which is used to determine the order of the higher order *** Comparing numerical simulations through the AMCBFM and the traditional Characteristic Basis Function Method(CBFM),the AMCBFM can guarantee the accuracy and void higher iteration,and the convergence performance is better than the CBFM in the lower order ***,by comparing numerical results of the AMCBFM and the method of moments(MoM),the AMCBFM can effectively reduce the size of the impedance matrix,and significantly reduce computing time.

关键词： Rough surface Electromagnetic scattering Adaptively Modified Characteristic Basis Function Method of moments

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Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable Medical Image Registration

SSRN

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SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： Image registration

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Function-structural Interaction with Progressive and Multi-level Feature Fusion for ADHD Classification

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IEEE Journal of Biomedical and Health Informatics 2025年

作者： Cao, Chunhong Li, Xingxing Wang, Mengyang Chen, Xiaolong Gao, Xieping Xiangtan University MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan411105 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

Individuals with Attention Deficit Hyperactivity Disorder (ADHD) exhibit intricate structural and functional interconnectivity across multiple brain regions. These patients demonstrate abnormal alterations in both respective modal brain regions and co-occurrent brain regions. Furthermore, there exist multi-level relationships between these abnormal brain structures and functions, encompassing hierarchical interactions between function-structural alterations as well as hierarchical progression from local regions to broader brain networks. However, most existing multi-modal ADHD classification approaches independently embed functional and structural data into separate spaces for information integration, often predominately focusing on uni-modal features. This approaches lead to a significant loss of features related to functiona-structural interaction relationships. Additionally, it is crucial for ADHD classification to accurately identify both uni-modal and co-occurrent abnormal alterations in brain regions which have hierarchical progression relationships. This study proposes a function-structural interaction multi-modal network with progressive and multi-level feature fusion (FSIPM) for ADHD classification. The main contributions are threefold: 1) An innovative function-structural interaction method is proposed to facilitate the mutual regulation of information across modalities, thereby relieving modal feature bias caused by integrated fusion. 2) A multi-level refinement framework is designed to promote the identification of both individual and co-occurrent abnormal brain regions. This progressive approach models the function-structural alterations of abnormal brain regions and the hierarchical relationships from local to brain networks, ensuring a deeper understanding of brain abnormalities. 3) Multi-level feature fusion aims to minimize the loss of details caused by consecutive sampling operations during the progressive process of the network, contributing to a

关键词： Modal analysis

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Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation

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Journal of Beijing Institute of Technology 2020年第1期29卷 110-119页

作者： Wenxia Bao Yaping Yang Dong Liang Ming Zhu Key Laboratory Intelligent Computing and Signal Processing of the Ministry of Education Anhui UniversityHefei 230039China

A multi-residual module stacked hourglass network(MRSH)was proposed to improve the accuracy and robustness of human body pose *** network uses multiple hourglass sub-networks and three new residual *** the hourglass sub-network,the large receptive field residual module(LRFRM)and the multi-scale residual module(MSRM)are first used to learn the spatial relationship between features and body parts at various *** the improved residual module(IRM)is used when the resolution is *** final network uses four stacked hourglass sub-networks,with intermediate supervision at the end of each hourglass,repeating high-low(from high resolution to low resolution)and low-high(from low resolution to high resolution)*** network was tested on the public datasets of Leeds sports poses(LSP)and MPII human *** experimental results show that the proposed network has better performance in human pose estimation.

关键词： human pose estimation residual learning image feature hourglass network

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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Object Recognition Based on Maximally Stable Extremal Region and Scale-Invariant Feature Transform

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Telkomnika (Telecommunication computing Electronics and Control) 2016年第2期14卷 622-629页

作者： Guo, Hongjun Chen, Lili Laboratory of Intelligent Information Processing Suzhou University Suzhou234000 China The Key Laboratory of Intelligent Computing and Signal Processing of MOE Anhui University Hefei230039 China

For the defect in describing affine and blur invariable of scale-invariant feature transform (SIFT) at large viewpoint variation, a new object recognition method is proposed in this paper, which used maximally stable extremal region (MSER) detecting MSERs and SIFT describing local feature of these regions. First, a new most stability criterion is adopt to improve the detection effect at irregular shaped regions and under blur conditions;then, the local feature descriptors of MSERs is extracted by the SIFT;and finally, the method proposed is comparing then correct rate of SIFT and the proposed through image recognition with standard test images. Experimental results show that the method proposed can still achieve more than 74% recognition correct rate at different viewpoint, which is better than SIFT. © Copyright 2016 Universitas Ahmad Dahlan.

关键词： Object recognition

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Complex-wave retrieval based on blind signal separation

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Chinese Optics Letters 2006年第1期4卷 11-14页

作者：柴晓冬周成鹏冯兆艳王银花左言胜 Department of Electronic Science and Technology University of Science and Technology of ChinaHefei 230026 Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230039 Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityHefei 230039

In tile process of the reconstruction of digital holography. the traditional methods of diffraction and filtration are commonly adopted to recover the original complex-wave signal. Influenced by twin-image and zero-order terms, the above-mentioned methods, however, either limit tile field of vision or result in the loss of the amplitude and phase. A new method for complex-wave retrieval is presented, which is based on blind signal separation. Three frames of holograms are captured by a charge coupled device （CCD） camera to form an observation signal. The term containing only amplitude and phase of complex-wave is separated, by means of independent component analysis, from the observation signal, which effectively eliminates the zero-order term. Finally. the complex-wave retrieval of pure phase wavefront is achieved. Experimental results show that this method can better recover the amplitude and phase of the original complex-wave even when there is a frequency spectrum mixture in the hologram.

关键词： Charge coupled devices Image reconstruction Independent component analysis signal interference signal processing Spectrum analysis

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