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Quantitative analysis of flow characteristics within tundishes using dye experiments and image processing techniques 认领 引用
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作者 Hao-Jian Duan Ding-Han Li Li-Feng Zhang 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第2期178-192,共15页
Image processing techniques were employed to analyze the ink dispersion process in a tundish water model,providing quantitative parameters to evaluate fluid flow dynamics.Key parameters,including filling time,dead are... Image processing techniques were employed to analyze the ink dispersion process in a tundish water model,providing quantitative parameters to evaluate fluid flow dynamics.Key parameters,including filling time,dead area fraction,area emptying time,peak concentration time,dead concentration fraction,and concentration emptying time,were introduced.Orthogonal tests were conducted to investigate the effects of dam spacing,height,and openings on tundish flow behavior.Results revealed that the dam spacing of 3760 mm yielded the shortest filling and peak concentration time,while the 4760 mm spacing minimized dead area and concentration fractions.Taller dams(620 mm)reduced dead area fractions but extended peak concentration time.Dams with openings demonstrated improved flow dynamics,reducing both dead area and concentration fractions,as well as emptying time.The consistency between dye experiments and residence time distribution experiments highlights the reliability of these parameters for optimizing tundish design and operation. 展开更多
关键词 Tundish Dye experiment Image processing technique Orthogonal test Dam
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Beyond conventional endoscopy:Image-enhanced techniques in quiescent ulcerative colitis assessment 认领 引用
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作者 Vedran Tomašić Petra Caćić +1 位作者 Neven Baršić Alen Bišćanin 《World Journal of Gastrointestinal Endoscopy》 2026年第1期59-67,共9页
Mucosal healing is an important therapeutic target in ulcerative colitis(UC)because it is associated with improved clinical outcomes and sustained remission.Conventional white-light endoscopy has limitations,including... Mucosal healing is an important therapeutic target in ulcerative colitis(UC)because it is associated with improved clinical outcomes and sustained remission.Conventional white-light endoscopy has limitations,including subjective interpretation,interobserver variability,and difficulty detecting residual microscopic inflammation despite an apparently healed mucosa.Image-enhanced endoscopy(IEE)techniques improve visualization of mucosal and vascular patterns,potentially enhancing the assessment of inflammation and healing in UC.Modalities such as narrow-band imaging,linked-color imaging,blue laser imaging,dual-red imaging,texture and color enhancement imaging,and iSCAN accentuate vascular structures and subtle color differences,allowing more precise differentiation between complete and partial healing.These methods correlate strongly with histological inflammation and better predict clinical relapse compared with conventional scoring systems such as the Mayo Endoscopic Subscore.Quantitative IEE-based indices,including the linked-color imaging index,Paddington International virtual ChromoendoScopy ScOre,and Mucosal Analysis of Inflammatory Gravity by iScan TE-c Image score,provide reproducible and objective measurements that reduce subjective variability.Next-generation endoscopic platforms combining advanced IEE technologies enable real-time,high-resolution evaluation of mucosal microarchitecture and vascular regeneration.This facilitates personalized management by detecting residual inflammation earlier,improving monitoring,optimizing treatment decisions,and ultimately enhancing long-term outcomes while lowering relapse rates in UC patients. 展开更多
关键词 Ulcerative colitis Image-enhanced endoscopy Virtual chromoendoscopy Endoscopic healing Histological healing Narrow-band imaging Linked color imaging
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Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks 认领 引用 被引量：4
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作者 Afshin Tatar Manouchehr Haghighi Abbas Zeinijahromi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第1期106-125,共20页
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist... The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications. 展开更多
关键词 Deep learning(DL) Image analysis Image data augmentation Convolutional neural networks(CNNs) Geological image analysis Rock classification Rock thin section(RTS)images
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Precision organoid segmentation technique(POST):accurate organoid segmentation in challenging bright-field images 认领 引用 被引量：2
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作者 Xuan Du Yuchen Li +5 位作者 Jiaping Song Zilin Zhang Jing Zhang Yanhui Li Zaozao Chen Zhongze Gu 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期80-93,I0013-I0016,共14页
Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of... Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process. 展开更多
关键词 Organoid Drug screening Deep learning Image segmentation
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Mock Observations for the CSST Mission:Multi-Channel Imager——Instrument Simulation 认领 引用 被引量：1
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作者 Zhao-Jun Yan Huan-Yuan Shan +40 位作者 Zhen-Ya Zheng Xi-Yan Peng Zhao-Xiang Qi Chun Xu Lin Lin Xin-Rong Wen Chun-Yan Jiang Li-Xin Zheng Jing Zhong Fang-Ting Yuan Zhen-Lei Chen Wei Chen Mao-Chun Wu Zhen-Sen Fu Ke-Xin Li Lin Nie Chao Liu Nan Li Qiao Wang Zi-Huang Cao Shuai Feng Guo-Liang Li Lei Wang Cheng-Liang Wei Xiao-Bo Li Zhang Ban Xun Yang Yu-Xi Jiang De-Zi Liu Yong-He Chen Xiao-Hua Liu Fang Xu Xue Cheng Yue Su Tong-Fang Duan Chao Qi Na Li Geng Zheng Chong Ma Jing Tang Ran Li 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2026年第2期78-97,共20页
The Chinese Space Station Survey Telescope(CSST),a two-meter aperture astronomical space telescope under China's manned space program,is equipped with multiple back-end scientific instruments.As an astronomical pr... The Chinese Space Station Survey Telescope(CSST),a two-meter aperture astronomical space telescope under China's manned space program,is equipped with multiple back-end scientific instruments.As an astronomical precision measurement module of the CSST,the Multi-Channel Imager(MCI)can cover a wide wavelength range from ultraviolet to near-infrared with three-color simultaneous high-precision photometry and imaging,which meets the scientific requirements for various fields.The diverse scientific objectives of MCI require not only a robust spaceborne platform,advanced optical systems,and observing facilities but also comprehensive software support for scientific operations and research.To this end,it is essential to develop realistic observational simulation software to thoroughly evaluate the MCI data stream and provide calibration tools for future scientific investigations.The MCI instrument simulation software will serve as a foundation for the development of the MCI data processing pipeline and will facilitate improvements in both hardware and software,as well as in the observational operation strategy,in alignment with the mission's scientific goals.In conclusion,we present a comprehensive overview of the MCI instrument simulation and some corresponding performances of the MCI data processing pipeline. 展开更多
关键词 methods numerical-telescopes-techniques image processing
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RCTUnet:a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction 认领 引用 被引量：1
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作者 Ting LI Yang LIU +10 位作者 Haikuan FENG Meiyan SHU Hao YANG Yuanyuan FU Xin XU Yinghao LIN Hongbo QIAO Wei GUO Xinming MA Lei SHI Jibo YUE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期517-536,共20页
Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between ... Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between fragmented residue and soil,compounded by variable illumination and shadows in field imagery,often lead to poor segmentation performance.To overcome these limitations,we introduce RCTUnet,a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation.RCTUnet’s architecture synergistically integrates three key components:(1)a ResNet50 backbone for deep,multi-scale feature extraction;(2)a convolutional block attention module(CBAM)to adaptively focus on salient residue features across both channel and spatial dimensions;and(3)a transformer-based global context fusion module(GCFM)to model long-range spatial dependencies,which is critical for interpreting heterogeneous residue patterns.We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations.Experimental results show that,compared to traditional models:(1)RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet,Unet++,DeepLabV3,segmentation network(SegNet),and fully convolutional network(FCN),with improvements of 3.24%,3.42%,4.88%,8.28%,and 6.05%in overall accuracy,respectively;(2)RCTUnet yields superior residue-soil segmentation performance,with increases in residue recall of 7.67%,7.37%,14.09%,27.05%,and 16.91%,respectively;(3)RCTUnet shows enhanced CRC estimation accuracy,achieving a root mean square error(RMSE)of 4.875,representing a 45.5%improvement over Unet(RMSE=8.941).These results demonstrate the efficacy of our hybrid approach,which combines deep hierarchical features,dual-domain attention,and global context modeling.RCTUnet provides a robust and reliable tool for automated CRC assessment,advancing the capabilities of in-field agricultural monitoring. 展开更多
关键词 Deep learning Crop residue cover Image segmentation Conservation tillage
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Experimental techniques for qualitative and quantitative characterization of shale oil reservoir space,minerals,and fluids 认领 引用 被引量：1
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作者 Siyu Chen Zhenkai Huang +6 位作者 Guangyou Zhu Xi Li Zhiyong Chen Ruilin Wang Yue Huang Shengnan Liu Bijun Huang 《Energy Geoscience》 EI CAS CSCD 2026年第2期1-16,共16页
Characterizing shale oil reservoirs encompassing pore space,mineralogy,and fluids is fundamental to effective exploration and development.Recent advances in experimental techniques have significantly improved both the... Characterizing shale oil reservoirs encompassing pore space,mineralogy,and fluids is fundamental to effective exploration and development.Recent advances in experimental techniques have significantly improved both the qualitative and quantitative analysis of these components;however,a comprehensive systematic review is lacking.High-resolution imaging technologies,such as Scanning Electron Microscopy(SEM),Field Emission Scanning Electron Microscopy(FE-SEM),and Focused Ion Beam Scanning Electron Microscopy(FIB-SEM),enable detailed visualization of pore structures.Gas adsorption and high-pressure mercury intrusion methods provide accurate pore-scale quantification.Moreover,techniques like X-ray Diffraction(XRD),X-ray Fluorescence Spectroscopy(XRF),and Electron Probe Microanalysis(EPMA)allow precise mineral identification and compositional analysis.Confocal Scanning Laser Microscopy(CSLM),Raman Spectroscopy,Nuclear Magnetic Resonance(NMR),and Rock Pyrolysis provide insights into fluid occurrence and content within shale reservoirs.Based on a comprehensive review of existing research,this study identifies several key future directions:(1)addressing the challenges of nanopore observation in reservoir space characterization while minimizing the impact of sample preparation on pore structure;(2)improving the accuracy of quantitative mineral analysis and developing advanced new technologies for the precise measurement of complex mineral compositions;(3)enhancing the fluid quantitative evaluation of fluids by more effectively restoring subsurface geological conditions.This paper presents a current synthesis and forward-looking perspective on experimental techniques supporting shale oil exploration,aiming to guide future research and technological innovation in this field. 展开更多
关键词 Experimental technique Shale oil Reservoir space Mineral analysis Fluid characterization Qualitative and quantitative analysis
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Measuring ice lens initiation in soils by particle image velocimetry 认领 引用 被引量：1
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作者 Jinfeng Li Jidong Teng +3 位作者 Haosong He Wenzhang Xu Jenisha Singh Sheng Zhang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期761-771,共11页
Ice lens initiation is the core issue in understanding the dynamic process of frost heave.However,there are still limitations to find an adequate criterion for describing the formation of ice lens.A series of one-dime... Ice lens initiation is the core issue in understanding the dynamic process of frost heave.However,there are still limitations to find an adequate criterion for describing the formation of ice lens.A series of one-dimensional freezing tests is designed using the particle image velocimetry(PIV)method to monitor the frost heave and ice lens formation.The results show that the conventional parameters,such as displacement and velocity,cannot be used to track the ice lens formation,while the strain can be employed to detect the ice lens formation and investigate the freezing change patterns.This study proposes strain as a new criterion for assessing ice lens initiation,applicable across various soil types and freezing conditions(constant freezing and ramped freezing).The strain change in the region where the ice lens forms is the largest during the freezing process.Additionally,strain curves at the top of the soil samples can reveal different freezing patterns and distinguish the first and second frost heave stages.This newly developed technology enables continuous,non-destructive monitoring of ice lens initiation across diverse conditions and soil types,enhancing data visualization and three-dimensional modeling of freezing parameters while improving traditional methods by directly measuring velocity and strain in frost heave investigations.The study is expected to enhance the research of ice lens criterion and provide a new perspective for monitoring the freezing process. 展开更多
关键词 Frost heave Ice lens initiation Particle image velocimetry(PIV) Strain
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A self-supervised learning-based method for simultaneous motion artifacts removal and denoising of OCTA images 认领 引用 被引量：1
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作者 Yuzeng Xu Songwen Xu +1 位作者 Di Yang Yanmei Liang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期123-139,共17页
Motion artifacts and noise are key factors that determine image quality in optical coherence tomography angiography(OCTA).Although deep learning has emerged as an effective method for artifacts removal and denoising,i... Motion artifacts and noise are key factors that determine image quality in optical coherence tomography angiography(OCTA).Although deep learning has emerged as an effective method for artifacts removal and denoising,its generalization capability remains limited,and it is difficult to handle images with both motion artifacts and noise.To address this issue,we designed a Swin Transformer-based multi-scale motion artifacts and noise parallel removal network(ST-MANPR)to learn the nonlinear mapping between images with motion artifacts and noise and images without them,thereby achieving simultaneous suppression of motion artifacts and noise.The proposed network integrates the Swin window attention module,channel attention module(CAM),and adaptive multi-scale convolution denoising module(AMS-CNN)to enhance its capability in processing complex image features.At the same time,a hybrid loss function combining wavelet transform(WT)and mean square error(MSE)was introduced to facilitate high-frequency detail restoration.In addition,we constructed a dataset of OCTA images with and without motion artifacts and noise at multiple intensity levels.The created dataset was applied to train the network and the test results were evaluated both visually and numerically.The experimental results show that the proposed network can effectively remove motion artifacts and noise in OCTA images simultaneously. 展开更多
关键词 Optical coherence tomographic angiography self-supervised learning motion artifacts removal image denoising generalization
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VSMI2-PANet:Versatile Scale-Malleable Image Integration and Patch Wise Attention Network With Transformer for Lung Tumour Segmentation Using Multi-Modal Imaging Techniques 认领 引用
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作者 Nayef Alqahtani Arfat Ahmad Khan +1 位作者 Rakesh Kumar Mahendran Muhammad Faheem 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第5期1376-1393,共18页
Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(... Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches. 展开更多
关键词 computational intelligence computer vision data fusion deep learning feature extraction image segmentation
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Synaptic pruning mechanisms and application of emerging imaging techniques in neurological disorders 认领 引用
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作者 Yakang Xing Yi Mo +1 位作者 Qihui Chen Xiao Li 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第5期1698-1714,共17页
Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience... Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders. 展开更多
关键词 chemokine complement experience-dependent driven synaptic pruning imaging techniques neuroglia signaling pathways synapse elimination synaptic pruning
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Multi-Scale Transformer for Image Restoration 认领 引用
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作者 Wuzhen Shi Youwei Pan +4 位作者 Chun Zhao Yuqing Liu Shaobo Zhang Heng Zhang Yang Wen 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期41-54,共14页
Although Transformer-based image restoration methods have demonstrated impressive performance,existing Transformers still insufficiently exploit multiscale information.Previous non-Transformer-based studies have shown... Although Transformer-based image restoration methods have demonstrated impressive performance,existing Transformers still insufficiently exploit multiscale information.Previous non-Transformer-based studies have shown that incorporating multiscale features is crucial for improving restoration results.In this paper,we propose a multiscale Transformer(MST)that captures cross-scale attention among tokens,thereby effectively leveraging the multiscale patch recurrence prior of natural images.Furthermore,we introduce a channel-gate feed-forward network(CGFN)to enhance inter-channel information aggregation and reduce channel redundancy.To simultaneously utilise global,local and multiscale features,we design a multitype feature integration block(MFIB).Extensive experiments on both image super-resolution and HEVC compressed video artefact reduction demonstrate that the proposed MST achieves state-of-the-art performance.Ablation studies further verify the effectiveness of each proposed module. 展开更多
关键词 computer vision image enhancement image processing image reconstruction image resolution
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LL-Refiner:Learning Adaptive Refinement for Ultra-High-Definition Low-Light Image Enhancement 认领 引用
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作者 Xunpeng Yi Qinglong Yan +4 位作者 Yibing Zhang Zongrong Li Hao Zhang Zhanchuan Cai Jiayi Ma 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1288-1300,共13页
Low-light image enhancement aims to address various degradations in low-light conditions,such as low illumination,noise pollution,color distortion,and missing scene content.With advances in digital imaging technology,... Low-light image enhancement aims to address various degradations in low-light conditions,such as low illumination,noise pollution,color distortion,and missing scene content.With advances in digital imaging technology,the resolution of captured images has seen substantial improvements.This poses new challenges in achieving good enhancement performance for multiscale details in ultra-high-definition images,as well as in managing the overhead for supporting the use of consumer-grade GPUs.In this paper,we proposed learning the adaptive refinement framework for ultra-high-definition image enhancement,termed LL-Refiner.It integrates the advantages of hierarchical adaptive refinement guided by coarse enhancement results to enhance the ultra-high-definition images.In detail,firstly,we conduct the coarse enhancement on the low resolution image by employing a Transformer-based coarse enhancement network.Secondly,the coarse enhancement output is fed into the adapted refinement injection.It assigns resolution-aware inputs as guidance to the corresponding adaptive aggregation module,which interacts with the backbone features of the adaptive refinement network.Ultimately,the adaptive refinement network incorporates a combination of hierarchical dense residual connection modules and lightweight convolutional modules at different resolution stages.Also,it integrates a multi-scale enhanced perceptual loss to progressively achieve ultra-high-definition image enhancement.Extensive experiments on ultra-high-definition image enhancement validate the effectiveness and superiority of the proposed method.Our code is publicly available at http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/XunpengYi/LL-Refiner. 展开更多
关键词 Adaptive refinement low-light image enhancement ultra-high-definition image
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基于人工智能Precise Image重建算法对头颅CT图像质量及辐射剂量的影响 认领 引用
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作者 廖甜 刘晓静 +5 位作者 宁先英 桂绅 孔祥闯 雷子乔 余建明 吴红英 《放射学实践》 CAS CSCD 北大核心 2026年第1期66-71,共6页
目的:评估Precise Image人工智能重建算法对头颅CT图像质量及辐射剂量的影响。方法:回顾性搜集行头颅CT平扫的80例患者,A组(40例)采用120 kV、150 mAs采集图像,同时采用Precise Image(sharp/standard/smooth/smoother)算法、iDose 4等... 目的:评估Precise Image人工智能重建算法对头颅CT图像质量及辐射剂量的影响。方法:回顾性搜集行头颅CT平扫的80例患者,A组(40例)采用120 kV、150 mAs采集图像,同时采用Precise Image(sharp/standard/smooth/smoother)算法、iDose 4等级算法进行图像重建;B组(40例)采用传统轴扫方案采集图像(120 kV、250 mAs扫描条件),采用iDose 4等级算法进行图像重建。对比不同剂量、不同重建方式下头颅CT检查图像质量及辐射剂量。结果:A组较B组CTDIvol、DLP、SSDE分别降低约55.02%、42.68%、59.22%(P<0.05)。A组随着重建算法等级的升高(sharp、standard、smooth、smoother),小脑、背侧丘脑及灰白质噪声SD值下降,信号噪声比(SNR)、对比噪声比(CNR)升高,且均高于同扫描条件下iDose 4算法,除sharp算法外差异均有统计学意义(P<0.05)。A组standard、smooth算法主观评分为(4.63±0.49)分、(4.27±0.38)分,两组均满足诊断需求;B组主观评分为(4.52±0.41)分。结论:Precise Image人工智能重建算法在保证图像质量的前提下可大大降低头颅CT辐射剂量。 展开更多
关键词 体层摄影术,X线计算机 人工智能 Precise Image 图像质量 辐射剂量
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FDEFusion:End-to-End Infrared and Visible Image Fusion Method Based on Frequency Decomposition and Enhancement 认领 引用
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作者 Ming Chen Guoqiang Ma +3 位作者 Ping Qi Fucheng Wang Lin Shen Xiaoya Pi 《Computers, Materials & Continua》 SCIE EI 2026年第4期817-839,共23页
In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,eff... In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMIpixel),and modified Visual Information Fidelity(VIFm). 展开更多
关键词 Infrared images visible images frequency decomposition restormer blocks global attention
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Clinical information prompt-driven retinal fundus image for brain health evaluation 认领 引用
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作者 Nuo Tong Ying Hui +10 位作者 Shui-Ping Gou Ling-Xi Chen Xiang-Hong Wang Shuo-Hua Chen Jing Li Xiao-Shuai Li Yun-Tao Wu Shou-Ling Wu Zhen-Chang Wang Jing Sun Han Lv 《Military Medical Research》 SCIE CAS CSCD 2026年第1期43-57,共15页
Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility... Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images. 展开更多
关键词 Retinal fundus image Brain volume Brain health Magnetic resonance imaging Deep learning Eye and brain connection
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MRWS:multi-stage RAW low-light image enhancement with wavelet information and SNR prior 认领 引用
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作者 Tao ZHANG Shiqi GAO +1 位作者 Hao WANG Xin ZHAO 《Optoelectronics Letters》 EI 2026年第4期250-256,共7页
For low-light image enhancement tasks,RAW images surpass RGB images due to their high information content,however,their noise and single-channel nature challenge feature extraction.Existing methods using multi-stage c... For low-light image enhancement tasks,RAW images surpass RGB images due to their high information content,however,their noise and single-channel nature challenge feature extraction.Existing methods using multi-stage convolutional neural network(CNN)frameworks struggle with global feature extraction,while single-stage CNN-transformer fusions often result in residual noise.To overcome these limitations,this paper introduces a multi-stage RAW image enhancement network combining CNN and transformer.Considering the characteristics inherent to the task,we devised a CNN-based denoising block for the denoising stage and incorporated wavelet information to enhance frequency features.A transformer-based correction block has been designed for the color and white balance recovery stage,with the white balance being adjusted dynamically using a signal-to-noise ratio(SNR)map.With this design,our method outperforms other state-of-the-art models in all metrics on the Sony and Fuji datasets of see-in-the-dark(SID),and achieves optimal structural similarity index measurement(SSIM)on the mono-colored raw(MCR)dataset. 展开更多
关键词 wavelet information multi stage feature extractionexisting SNR prior raw image rgb images feature extractionwhile low light image
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Fracture response patterns in deep to ultra-deep tight sandstones:A comparison based on core and borehole images 认领 引用
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作者 Yu Du Hu-Cheng Deng +7 位作者 Xiao-Fei Hu Hao-Tian Zhang Hong-Hui Wang Cui-Li Wang Mao-Xin Liu Chen-Yang Zhao Shang-Rong Guo Zi-Yun Zheng 《Petroleum Science》 SCIE EI CAS CSCD 2026年第2期541-562,共22页
Natural fractures serve as the primary storage spaces and flow pathways in deep to ultra-deep tight sandstone reservoirs,directly influencing hydrocarbon accumulation,preservation,and production.Borehole images offer ... Natural fractures serve as the primary storage spaces and flow pathways in deep to ultra-deep tight sandstone reservoirs,directly influencing hydrocarbon accumulation,preservation,and production.Borehole images offer intuitive,continuous,and high-resolution identification of natural fractures along the entire borehole.However,relying solely on complete sinusoidal curves from borehole images for fracture identification may lead to omissions,as it overlooks cases where these curves are incomplete or truncated.To address the problems and deficiencies in fracture identification,this study systematically classifies borehole image feature patterns based on core-to-log spatial position restoring.A bidirectional comparison is conducted between natu ral fractures in cores and the fracture image features in borehole images.A quantitative relationship between fracture dip angle,thin layer thickness and borehole radius was established,accompanied by a mathematical expression describing the fracture curve morphology was proposed.These findings enabled the development of an imaging response pattern for natural fractures in deep and ultra-deep tight sandstone reservoirs,incorporating key parameters such as dip angle,through-layer connectivity,and spatial position within the borehole.In the Bashijiqike-Baxigai tight-sandstone reservoirs of the Bozi-Dabei area,we estimate that approximately 24%of coreobserved fractures display distinct linear-pattern features on borehole images,whereas approximately 91%of borehole images features can be correlated with fractures observed in core.Fracture identification rates for natural fractures increased by 17%in water-based mud and by 3%in oil-based mud through the application of the natural fracture image response pattern.Moreover,this study analyzes the deviations in the matching between core fractures and image features.Finally,we further discuss the common sources of error in natural fracture identification using borehole images from multiple perspectives,including missing core responses,inconsistencies between core and borehole image features,distortion of fracture chord curve,inaccurate fracture count,misclassification of fractures,and variations in interpretation under different mud systems.The research addresses the blind spots of traditional methods in fracture identification within thin layers,not only enhancing the detection rate of natural fractu res but also further improving the accuracy of fractu re recognitio n.At the same time,it will contribute to the optimization of fracture characterization,reservoir evaluation,and production forecasting,providing a more reliable data foundation for exploration and development under complex geological conditions. 展开更多
关键词 Borehole images Natural fracture Fracture image features Fracture response pattern Fracture identification Tight sandstone reservoir
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Continuous image representation based on deep learning for reducing interpolation bias in DIC 认领 引用
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作者 Lianpo Wang Zhaoyang Lei 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第3期51-60,共10页
After more than forty years of development,the accuracy of digital image correlation(DIC)methods has reached an extremely high level.However,the interpolation bias of DIC has not been resolved.With the flourishing of ... After more than forty years of development,the accuracy of digital image correlation(DIC)methods has reached an extremely high level.However,the interpolation bias of DIC has not been resolved.With the flourishing of deep learning in the field of image superresolution,it has become possible to use deep learning-based image superresolution methods to reduce DIC interpolation bias.To achieve this goal,this paper improves the local implicit image function(LIIF)method based on the characteristics of speckle images to obtain local implicit image function suitable for speckle images(LIIF-S),achieving continuous image representation and arbitrary resolution interpolation.Subsequently,LIIF-S is used as the interpolation algorithm of the inverse compositional-Gaussian Newton method to reduce the interpolation bias.The simulation experiment results show that LIIF-S not only improves the accuracy by more than one order of magnitude compared to traditional interpolation algorithms but also that the interpolation bias does not have sinusoidal characteristics.In addition,the effectiveness and generalization of the LIIF-S method in unseen real-world scenarios have also been demonstrated through physical experiments.The code and dataset are publicly available at http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/LianpoWang/SLIIF. 展开更多
关键词 Digital image correlation Deep learning Interpolation bias Local implicit image functions Continuous image representation Image superresolution
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TransFM:Visible-to-Infrared Image Translation via Flow Matching 认领 引用
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作者 Meiqi Gong Hao Zhang +1 位作者 Bingwei Hui Jiayi Ma 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1239-1241,共3页
Dear Editor,Due to the scarcity of high-quality infrared data,translating visible images to infrared has become a practical solution to meet the growing demand for infrared images in low-light and adverse conditions.D... Dear Editor,Due to the scarcity of high-quality infrared data,translating visible images to infrared has become a practical solution to meet the growing demand for infrared images in low-light and adverse conditions.Due to the large modality gap and limited prior information,existing visible-to-infrared(VIS-to-IR)image translation methods often struggle with poor structural preservation,unclear cross-modal correspondence,and loss of thermal details. 展开更多
关键词 visible infrared visible images structural preservation flow matching modality gap cross modal correspondence infrared images thermal details
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