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Efficient misalignment correction for annular LED arrays in intensity diffraction tomography

Optics Letters 2025年第9期50卷 2880-2883页

作者： Zhu, Ruizhi Zou, Wenjie Zhang, Runnan Zhou, Zihao Sun, Jiasong Chen, Qian Zhou, Ning Zuo, Chao Nanjing University of Science and Technology Jiangsu Nanjing210094 China Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Jiangsu Nanjing210094 China

Intensity diffraction tomography (IDT) is a powerful label-free 3D microscopy technique capable of reconstructing the 3D refractive index (RI) of biological samples using angled programmable illumination and computational algorithms. Despite its potential for high spatiotemporal resolution imaging, its practical performance is highly sensitive to precise alignment between experimental setups and algorithmic models. In this Letter, we present an efficient misalignment correction method for annular LED arrays in IDT (mcIDT), incorporating an improved Fourier–Mellin transform (FMT) algorithm with enhanced noise resistance for wavelength and positional corrections. Furthermore, a global positional misalignment model is optimized using the least squares method, enabling robust correction even in the presence of significant misalignments. By integrating algorithmic "calibration" with physical position "correction," mcIDT significantly enhances both the resolution and accuracy of 3D RI reconstruction. Experimental results on resolution targets and biological samples, such as HepG2 and C166 cells, demonstrate its superior resolution and robustness in label-free, high spatiotemporal resolution 3D volumetric imaging. The proposed mcIDT offers a flexible and efficient solution for label-free computational microscopy across diverse biological and industrial applications. © 2025 Optica Publishing Group.

关键词： Least squares approximations

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Super resolution imaging based on sub-pixel scanning with beam shifter devices 7

Super resolution imaging based on sub-pixel scanning with be...

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7th Optics Young Scientist Summit, OYSS 2024

作者： Liang, Kunyao Wang, Bowen Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

We proposed an active micro-scanning super-resolution imaging method. A beam shifter is placed between the lens and the camera, and change the tilt angle of the beam shifter's glass plate to achieve lateral movement of the light. Tilting the beam shifter at a tiny angle in X and Y directions to realize any geometric scanning scheme. By collecting low-resolution image sequences with different shifts in the same scene, a super-resolution algorithm can retrieve the high-resolution. In this work, we adopted the adaptive regularization parameter tuning(APRT) method to reconstruct the object details. We design and build a prototype of the beam-shifter-based imaging system, and the experiment can demonstrate a 1.5 times imaging resolution improvement of the algorithm. © 2025 SPIE.

关键词： imaging systems

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PD-L1 targeted iron oxide SERS bioprobe for accurately detecting circulating tumor cells and delineating tumor boundary

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Chinese Chemical Letters 2025年第1期36卷 408-414页

作者： Ting Pan Dinghu Zhang Guomei You Xiaoxia Wu Chenguang Zhang Xinyu Miao Wenzhi Ren Yiwei He Lulu He Yuanchuan Gong Jie Lin Aiguo Wu Guoliang Shao Department of Interventional Radiology Zhejiang Cancer HospitalInstitute of Basic Medicine and Cancer(IBMC)Chinese Academy of SciencesHangzhou 310022China Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology Cixi Institute of Biomedical EngineeringInternational Cooperation Base of Biomedical Materials Technology and ApplicationChinese Academy of Science(CAS)Key Laboratory of Magnetic Materials and Devices and Zhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringCASNingbo 315201China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China

Early diagnosis and accurate boundary delineation are the key steps of tumor precision *** tumor cells(CTCs)detection of liquid biopsy can provide abundant information for early diagnosis of *** detection specificity and good enrichment features are two key factors for CTCs accurate identification in peripheral blood *** this purpose,iron oxide(IO)-based surface-enhanced Raman scattering(SERS)bioprobes with good biocompatibility,high detection sensitivity,remarkable detection specificity,and good enrichment efficiency,were developed for detecting different types of *** SERS bioprobes combined with programmed death ligand-1(PD-L1)antibody are regarded as an effective way to boost the targeting ability and detection specificity,benefiting for accurately capturing and identifying rare *** types of CTCs with different PD-L1 expression were accurately distinguished among white blood cells via high-resolution SERS mapping images and stable Raman ***,CTCs blood samples obtained from the triple negative breast cancer patients were also successfully recognized compared to that of health people,indicating IO@AR@PDA-a PD-L1 SERS bioprobe possessed great potential for CTCs detection in liquid ***,IO-based bioprobe exhibited excellent dual-modal imaging abilities of high-resolution SERS imaging mode and microimaging magnetic resonance imaging *** two highly complementary imaging modes endowed IO-based bioprobes unrivalled capacity in tumor boundary differentiation,supporting tumor accurate resection and precise *** our best knowledge,this is the first time that biocompatible IO-based SERS bioprobes without noble metal element were reported not only for CTCs accurate detection,but also for precise tumor boundary delineation,showing great advantages in tumor diagnosis and treatment.

关键词： Circulating tumor cells Surface-enhanced Raman scattering PD-L1 Iron oxide bioprobe Dual-modal imaging Tumor boundaries delineation

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Two-stage unmanned aerial vehicle localization method based on multi-category semantic segmentation and template matching

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Jin, Hu Ren, Kan Chen, Qian Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing China

With advancements in unmanned aerial vehicle (UAV) technology, UAV applications are rapidly growing, and their operations are becoming increasingly intelligent. Localization of UAVs commonly relies on global navigation satellite systems combined with inertial navigation systems through sensor fusion. However, this approach is vulnerable to significant risks, such as signal spoofing. In military conflicts, signal spoofing by hackers poses a severe security threat with potentially catastrophic outcomes. To address this issue, we propose a two-stage vision-based UAV localization method. This approach utilizes multi-category semantic segmentation and template matching to establish a connection between heterogeneous sensors. Experimental results demonstrate the method's effectiveness in accurately identifying the UAV's location within extensive geographical areas captured in remote sensing images. In addition, it achieves high precision in aligning UAV locations with Baidu maps, offering robust and accurate localization capabilities. © 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Semantic Segmentation

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Structured illumination microscopy using DMD based on high-modulation coefficient mapping-enabled illumination parameter calibration and principal component analysis 4

Structured illumination microscopy using DMD based on high-m...

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4th International Computational imaging Conference, CITA 2024

作者： Han, Xinyu Lu, Dongqin Qian, Jiaming Zuo, Chao Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China

ISBN: (纸本)9781510688834

With the advantages of low phototoxicity, high photon efficiency, and rapid imaging capabilities, Structured Illumination Microscopy (SIM) has been of paramount importance for observation of living cells in the field of life sciences. Traditional SIM systems are constrained by their substantial size, intricate structure, and high cost, thereby impeding their widespread adoption. In contrast, SIM systems based on Digital Micromirror Devices (DMD) have overcome these limitations, offering portability and facilitating broader applications. However, the deficiency of DMD in high-frequency stripe modulation pose a challenge to achieving high-quality super-resolution imaging. To overcome the conflict between system complexity and resolution, an illumination parameter calibration method based on high modulation coefficient mapping is proposed. This approach constructs a mathematical model correlating initial wave vectors of high-frequency, low-modulation illumination patterns with those easily accessible of low-frequency, high-modulation ones. We devise a rapid and robust sub-pixel illumination parameter estimation strategy based on Principal Component Analysis (PCA), ensuring high-accuracy super-resolution imaging of live cells. Our DMD-SIM system accurately predicts high-frequency illumination parameters, achieving 1.9x super-resolution in roundworm and mitochondrial samples, while occupying 70% less space compared to interferometric SIMs. © 2025 SPIE.

关键词： Digital devices

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Self-BSR: Self-Supervised Image Denoising and Destriping Based on Blind-Spot Regularization

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Qu, Chao Chen, Zewei Zhang, Jingyuan Chen, Xiaoyu Han, Jing Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing210094 China

Digital images captured by unstable imaging systems often simultaneously suffer from random noise and stripe noise. Due to the complex noise distribution, denoising and destriping methods based on simple handcrafted priors may leave residual noise. Although supervised methods have achieved some progress, they rely on large-scale noisy-clean image pairs, which are challenging to obtain in practice. To address these problems, we propose a self-supervised image denoising and destriping method based on blind-spot regularization, named Self-BSR. This method transforms the overall denoising and destriping problem into a modeling task for two spatially correlated signals: image and stripe. Specifically, blind-spot regularization leverages spatial continuity learned by the improved blind-spot network to separately constrain the reconstruction of image and stripe while suppressing pixel-wise independent noise. This regularization has two advantages: first, it is adaptively formulated based on implicit network priors, without any explicit parametric modeling of image and noise;second, it enables Self-BSR to learn denoising and destriping only from noisy images. In addition, we introduce the directional feature unshuffle in Self-BSR, which extracts multi-directional information to provide discriminative features for separating image from stripe. Furthermore, the feature-resampling refinement is proposed to improve the reconstruction ability of Self-BSR by resampling pixels with high spatial correlation in the receptive field. Extensive experiments on synthetic and real-world datasets demonstrate significant advantages of the proposed method over existing methods in denoising and destriping performance. © 1991-2012 IEEE.

关键词： Pixels

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Detector with Classifier2: An end-to-end Multi-Stream Feature Aggregation Network for Fine-Grained Object Detection in Remote Sensing Images

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IEEE Transactions on Image Processing 2025年 34卷 2707-2720页

作者： Zheng, Shangdong Wu, Zebin Xu, Yang He, Chengxun Wei, Zhihui Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense China

Fine-grained object detection (FGOD) fundamentally comprises two primary tasks: object detection and finegrained classification. In natural scenes, most FGOD methods benefit from higher instance resolution and fewer environmental variation, attributing more commonly associated with the latter task. In this paper, we propose an unified paradigm named Detector with Classifier2 (DC2), which provides a holistic paradigm by explicitly considering the end-to-end integration of object detection and fine-grained classification tasks, rather than prioritizing one aspect. Initially, our detection sub-network is restricted to only determining whether the proposal is a coarse-category and does not delve into the specific sub-categories. Moreover, in order to reduce redundant pixel-level calculation, we propose an instance-level feature enhancement (IFE) module to model the semantic similarities among proposals, which poses great potential for locating more instances in remote sensing images (RSIs). After obtaining the coarse detection predictions, we further construct a classification sub-network, which is built on top of the former branch to determine the specific sub-categories of the aforementioned predictions. Importantly, the detection network is performed on the complete image, while the classification network conducts secondary modeling for the detected regions. These operations can be denoted as the global contextual information and local intrinsic cues extractions for each instance. Therefore, we propose a multi-stream feature aggregation (MSFA) module to integrate global-stream semantic information and local-stream discriminative cues. Our whole DC2 network follows an end-to-end learning fashion, which effectively excavates the internal correlation between detection and finegrained classification networks. We evaluate the performance of our DC2 network on two benchmarks SAT-MTB and HRSC2016 datasets. Importantly, our method achieves the new state-of-theart results com

关键词： Object detection

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Single-shot super-resolved fringe projection profilometry(SSSR-FPP):100,000 frames-per-second 3D imaging with deep learning

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光：科学与应用（英文版） 2025年第2期14卷 551-563页

作者： Bowen Wang Wenwu Chen Jiaming Qian Shijie Feng Qian Chen Chao Zuo Smart Computational Imaging Laboratory(SCILab) Nanjing University of Science and TechnologyNanjingJiangsu ProvinceChina Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu ProvinceChina Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu ProvinceChina

To reveal the fundamental aspects hidden behind a variety of transient events in mechanics,physics,and biology,the highly desired ability to acquire three-dimensional(3D)images with ultrafast temporal resolution has been long *** one of the most commonly employed 3D sensing techniques,fringe projection profilometry(FPP)reconstructs the depth of a scene from stereo images taken with sequentially structured ***,the imaging speed of current FPP methods is generally capped at several kHz,which is limited by the projector-camera hardware and the number of fringe patterns required for phase retrieval and *** we report a novel learning-based ultrafast 3D imaging technique,termed single-shot super-resolved FPP(SSSR-FPP),which enables ultrafast 3D imaging at 100,000 ***-FPP uses only one pair of low signal-to-noise ratio(SNR),low-resolution,and pixelated fringe patterns as input,while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural *** approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras,while"regenerating"the lost spatial resolution through deep *** demonstrate the high spatio-temporal resolution of SSSR-FPP,we present 3D videography of several transient scenes,including rotating turbofan blades,exploding building blocks,and the reciprocating motion of a steam engine,etc.,which were previously challenging or even impossible to capture with conventional *** results establish SSSR-FPP as a significant step forward in the field of 3D optical sensing,offering new insights into a broad spectrum of dynamic processes across various scientific disciplines.

关键词： projection learning imaging fringe second deep with frames profilometry single-shot

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Multi-scale Feature Interaction and Adaptive Experts for Panoptic Segmentation in Remote Sensing Images

Multi-scale Feature Interaction and Adaptive Experts for Pan...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Zhenkun Sun Jia Liu Wenhua Zhang Fang Liu Jingxiang Yang Liang Xiao Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Panoptic segmentation unifies the traditional tasks of instance and semantic segmentation. It plays a crucial role in the field of remote sensing; however, it encounters challenges in recognizing small objects and in the model’s ability to generalize across complex scenes. In this paper, we introduce the MFIAE framework to address two specific challenges: a multi-scale interactive attention fusion (MSIAF) module and an adaptive disturbance sparse mixture-of-experts (ADSMoE) module based on Transformer. The MSIAF module is designed to fully utilize the rich contextual information captured by low-resolution features while simultaneously utilizing the advantages of high-resolution features to enhance small object segmentation. In the ADSMoE module, adaptive noise is introduced to disturb the expert selection in order to enhance the randomness and exploration of the model when it comes to selecting experts, thereby improving its capacity for generalization and robustness. Additionally, it also reduces the computational overhead and the model complexity. The experimental results demonstrate that our approach achieves state-of-the-art performance on the BSB Aerial dataset.

关键词： Adaptation models Computational modeling Semantic segmentation Noise Object segmentation Transformers Robustness Sensors Speech processing Remote sensing

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Infrared small target detection based on spatial attention density peaks searching

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Optoelectronics Letters 2025年第2期21卷 90-96页

作者： RAO Junmin LU Siyu LIU Shijian LI Fanming CAS Key Laboratory of Infrared System Detection and Imaging Technology Shanghai Institute of Technical PhysicsShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China

The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and *** this paper,we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection ***,the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared ***,the image is processed by ***,an adaptive window selection scheme is applied to obtain window templates for the target scale ***,the small target feature is used to enhance the target and suppress the ***,the true targets are segmented through a *** experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms,the proposed method not only has better detection accuracy,but also has reasonable time consumption.

关键词： template searching Infrared

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