久久精品一区二区免费播放-五月婷婷久久草-97精品超碰一区二区三区-国产精品99久久久精品无码-中文字幕人成乱码在线观看-国产SUV精品一区二区69-国精品无码人妻一区二区三区-亚洲蜜桃精久久久久久久久久久久-欧美综合自拍亚洲综合图-久久久国产精品人人片-久久亚洲精品AV成人无码-国产AV一区二区三区最新精品-亚洲熟女乱色综合亚洲图片,一本到不卡无码免费在线,国产精品国产三级国产AV麻豆,中国丰满熟女片免费观,亚洲国产精品成人软件,神马影院手机在线观看,欧美日韩久久综合,久久久久久久久久久无码,国产熟妇久久精品亚洲熟女图片,日韩女人一级片,欧美久成人做爰视频,麻豆入口在线看,九九精品久久,国产香蕉视频一直看一直爽,高清肉动漫在线观看,十八嫩内射,久碰久碰,欧洲亚洲精品A片久久99动漫,黄色片网站91,色情韩国电影在线线看,蜜桃精品免费久久久久影院,欧美激情四射一区二区在线,国产亚洲精品97,自偷自拍亚洲综合精品第一页,久久免费看少妇高潮A片特黄中,无码乱人伦一区二区亚洲一,WWW国产内插视频,国产精品久久久久无码人妻网站,国产男女猛烈无遮挡A片软件,久久久亚洲精品一区二区三区,韩国三级巜双乳紧扣

2024

2024

  • Record 361 of

    Title:Swin-CDSA: The Semantic Segmentation of Remote Sensing Images Based on Cascaded Depthwise Convolution and Spatial Attention Mechanism
    Author Full Names:Kang, Yuhan; Ji, Jian; Xu, Hekai; Yang, Yong; Chen, Peng; Zhao, Hui
    Source Title:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
    Language:English
    Document Type:Article
    Abstract:As an important task in remote sensing image processing, semantic segmentation of remote sensing images has broad application prospects in many fields such as disaster warning and rescue, environmental protection, and road planning. Research on semantic segmentation of remote sensing images based on deep learning has made some progress, but there are still problems such as poor perception of small object features, loss of detailed information in deep feature extraction, and imprecise segmentation contours of small objects. To this end, we propose a new remote sensing semantic segmentation model Swin-CDSA, which copes these problems to some extent by designing cascaded deep convolutional modules (CDCMs) and spatial attention mechanisms (SAMs). CDCM extracts multiscale features by using multilayer convolutions with different layers but parallel fixed small-sized kernels, while SAM supplements the model's understanding of local and global information through a dual attention mechanism. We conducted experiments on the Potsdam and LoveDA datasets and achieved good results.
    Addresses:[Kang, Yuhan; Ji, Jian; Xu, Hekai; Yang, Yong; Chen, Peng] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China; [Zhao, Hui] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
    Affiliations:Xidian University; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS
    Publication Year:2024
    Volume:21
    Article Number:3003405
    DOI Link:http://dx.doi.org/10.1109/LGRS.2024.3431638
    數(shù)據(jù)庫ID(收錄號):WOS:001283693700005
  • Record 362 of

    Title:Hybrid Fiber-Single Crystal Fiber Chirped-Pulse Amplification System Emitting More Than 1.5 GW Peak Power With Beam Quality Better Than 1.3
    Author Full Names:Li, Feng; Zhao, Wei; Li, Qianglong; Zhao, Hualong; Wang, Yishan; Yang, Yang; Wen, Wenlong; Cao, Xue
    Source Title:JOURNAL OF LIGHTWAVE TECHNOLOGY
    Language:English
    Document Type:Article
    Keywords Plus:FEMTOSECOND; AMPLIFIER; KW; LASERS
    Abstract:A hybrid chirped pulse amplification system composed by the monolithic fiber pre-amplifier and a two-stage single-pass single crystal fiber amplifier was demonstrated. A maximum power of 68 W at the repetition rate of 100 kHz was obtained. The laser pulses were amplified and then compressed using a 1600 line/mm grating pair compressor. A short pulse duration of 358 fs and a power of 54 W were obtained at 100 kHz, corresponding to a peak power of 1.508 GW, to the best of our knowledge, this is the highest peak power ever obtained from single crystal fiber at repetition rate above 100 kHz due to the consideration of the third order dispersion which was engraved in the stretcher and the tuning capacity of higher-order dispersion compensation of chirped fiber Bragg grating. Additionally, the beam quality better than 1.3 was obtained. This high peak power CPA system with excellent comprehensive parameters will find various applications in scientific research and industrial applications.
    Addresses:[Li, Feng; Zhao, Wei; Li, Qianglong; Zhao, Hualong; Wang, Yishan; Yang, Yang; Wen, Wenlong; Cao, Xue] Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; State Key Laboratory of Transient Optics & Photonics
    Publication Year:2024
    Volume:42
    Issue:1
    Start Page:381
    End Page:385
    DOI Link:http://dx.doi.org/10.1109/JLT.2023.3312399
    數(shù)據(jù)庫ID(收錄號):WOS:001129777400014
  • Record 363 of

    Title:Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images
    Author Full Names:Li, Xuemei; Wang, Xing; Ye, Huping; Qiu, Shi; Liao, Xiaohan
    Source Title:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
    Language:English
    Document Type:Article
    Keywords Plus:COASTLINE EXTRACTION; NETWORK
    Abstract:The demarcation between the sea and the land, commonly referred to as the coastline, is of paramount importance for the dynamic monitoring of its alterations. This monitoring is essential for the effective utilization of marine resources and the conservation of the ecological environment. Addressing the challenges posed by the extensive expanse of coastal lines, which can complicate their acquisition and processing, this study utilizes remote sensing imagery to introduce an algorithm for coastal line segmentation. The algorithm integrates multiple networks to enhance its effectiveness. Innovations encompass the development of an extraction algorithm for coastal lines that are as follows. First, utilize an attention-guided conditional generative adversarial network (AC-GAN) model, which redefines the task of image segmentation by framing it as a style transformation problem. Second, a strategy for coastal line segmentation utilizes Dense Swin Transformer Unet (DSTUnet) to construct a densely structured model. This approach integrates Transformer to prioritize focal regions, thereby enhancing image and semantic interpretation. Third, a transfer learning framework is proposed to integrate multiple features, leveraging the strengths of different networks to achieve accurate segmentation of coastal lines. The study introduced two datasets, and the experimental results confirm that parallel network configurations and asymmetric weighting are superior in achieving optimal results, with an area overlap measure (AOM) score of 85%, outperforming the Unet by 5%.
    Addresses:[Li, Xuemei] Chengdu Univ Technol, Sch Mech & Elect Engn, Chengdu 610059, Peoples R China; [Wang, Xing] Natl Inst Measurement & Testing Technol, Elect Res Inst, Chengdu 610021, Peoples R China; [Ye, Huping; Liao, Xiaohan] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Ye, Huping] Chinese Acad Sci, Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China; [Qiu, Shi] Xian Inst Opt & Precis Mech, Chinese Acad Sci, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China; [Liao, Xiaohan] Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China
    Affiliations:Chengdu University of Technology; National Institute of Measurement & Testing Technology; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences
    Publication Year:2024
    Volume:62
    Article Number:4208312
    DOI Link:http://dx.doi.org/10.1109/TGRS.2024.3435963
    數(shù)據(jù)庫ID(收錄號):WOS:001288457800005
  • Record 364 of

    Title:Biomedical Image Segmentation Using Denoising Diffusion Probabilistic Models: A Comprehensive Review and Analysis
    Author Full Names:Liu, Zengxin; Ma, Caiwen; She, Wenji; Xie, Meilin
    Source Title:APPLIED SCIENCES-BASEL
    Language:English
    Document Type:Review
    Keywords Plus:CONVOLUTIONAL NEURAL-NETWORKS; PREDICTION; ALGORITHM; ENTROPY; CANCER
    Abstract:Biomedical image segmentation plays a pivotal role in medical imaging, facilitating precise identification and delineation of anatomical structures and abnormalities. This review explores the application of the Denoising Diffusion Probabilistic Model (DDPM) in the realm of biomedical image segmentation. DDPM, a probabilistic generative model, has demonstrated promise in capturing complex data distributions and reducing noise in various domains. In this context, the review provides an in-depth examination of the present status, obstacles, and future prospects in the application of biomedical image segmentation techniques. It addresses challenges associated with the uncertainty and variability in imaging data analyzing commonalities based on probabilistic methods. The paper concludes with insights into the potential impact of DDPM on advancing medical imaging techniques and fostering reliable segmentation results in clinical applications. This comprehensive review aims to provide researchers, practitioners, and healthcare professionals with a nuanced understanding of the current state, challenges, and future prospects of utilizing DDPM in the context of biomedical image segmentation.
    Addresses:[Liu, Zengxin; Ma, Caiwen; She, Wenji; Xie, Meilin] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Liu, Zengxin] Univ Chinese Acad Sci, Sch Optoelect, Beijing 101408, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:14
    Issue:2
    Article Number:632
    DOI Link:http://dx.doi.org/10.3390/app14020632
    數(shù)據(jù)庫ID(收錄號):WOS:001149358200001
  • Record 365 of

    Title:Study on Stray Light Testing and Suppression Techniques for Large-Field of View Multispectral Space Optical Systems
    Author Full Names:Lu, Yi; Xu, Xiping; Zhang, Ning; Lv, Yaowen; Xu, Liang
    Source Title:IEEE ACCESS
    Language:English
    Document Type:Article
    Keywords Plus:WIDE-FIELD; ELIMINATION; DESIGN
    Abstract:To evaluate the ability of space optical systems to suppress off-axis stray light, this paper proposes a stray light testing method for large-field of view, multispectral spatial optical systems based on point source transmittance (PST). And a stray light testing platform was developed using a high-brightness simulated light source, large-aperture off-axis reflective collimator, high-precision positioning mechanism and a double column tank to evaluate the stray light PST index of spatial optical system. On the basis of theoretical analyses, a set of calibration lenses and stray light elimination structures such as hoods, baffle and stop are designed for the accuracy calibration of stray light testing systems. The theoretical PST values of the calibration lens at different off-axis angles are analyzed by Trace Pro software simulation and compared with the measured values to calibrate the accuracy of the system. The testing results show that the PST measurement range of the system reaches 10(-3)similar to 10(-10) when the off-axis angles of the calibration lens are in the range of +/- 5 degrees similar to +/- 60 degrees. The stray light test system has the advantages of wide working band, high automation and large dynamic range, and its test results can be used in the correction of lens hood and other applications.
    Addresses:[Lu, Yi; Xu, Xiping; Zhang, Ning; Lv, Yaowen] Changchun Univ Sci & Technol, Natl Demonstrat Ctr Expt Optoelect Engn Educ, Sch Optoelect Engn, Changchun 130022, Peoples R China; [Xu, Liang] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
    Affiliations:Changchun University of Science & Technology; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS
    Publication Year:2024
    Volume:12
    Start Page:33938
    End Page:33948
    DOI Link:http://dx.doi.org/10.1109/ACCESS.2024.3369471
    數(shù)據(jù)庫ID(收錄號):WOS:001178226700001
  • Record 366 of

    Title:Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
    Author Full Names:Qin, Xing; Gao, Xin; Yang, Xiaoxu; Xie, Meilin
    Source Title:PHOTONICS
    Language:English
    Document Type:Article
    Keywords Plus:AFFINE SYSTEMS; ALGORITHM; IMAGE; MAGNITUDE; L-2(R-D); RECOVERY
    Abstract:This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt-pepper noise.
    Addresses:[Qin, Xing; Yang, Xiaoxu; Xie, Meilin] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Qin, Xing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Gao, Xin] Beijing Inst Tracking & Telecommun Technol, Beijing 100094, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:11
    Issue:1
    Article Number:71
    DOI Link:http://dx.doi.org/10.3390/photonics11010071
    數(shù)據(jù)庫ID(收錄號):WOS:001151554300001
  • Record 367 of

    Title:Attention Network with Outdoor Illumination Variation Prior for Spectral Reconstruction from RGB Images
    Author Full Names:Song, Liyao; Li, Haiwei; Liu, Song; Chen, Junyu; Fan, Jiancun; Wang, Quan; Chanussot, Jocelyn
    Source Title:REMOTE SENSING
    Language:English
    Document Type:Article
    Keywords Plus:REFLECTANCE RECOVERY; COVER
    Abstract:Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods.
    Addresses:[Song, Liyao] Xian Technol Univ, Inst Artificial Intelligence & Data Sci, Xian 710021, Peoples R China; [Li, Haiwei; Chen, Junyu; Wang, Quan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Liu, Song] Nanchang Hangkong Univ, Sch Measuring & Opt Engn, Nanchang 330063, Peoples R China; [Fan, Jiancun] Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, GIPSA Lab, CNRS, F-38000 Grenoble, France
    Affiliations:Xi'an Technological University; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Nanchang Hangkong University; Xi'an Jiaotong University; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
    Publication Year:2024
    Volume:16
    Issue:1
    Article Number:180
    DOI Link:http://dx.doi.org/10.3390/rs16010180
    數(shù)據(jù)庫ID(收錄號):WOS:001141352200001
  • Record 368 of

    Title:Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS
    Author Full Names:Su, Yunhao; Han, Junfeng; Ma, Caiwen; Wu, Jianming; Wang, Xuan; Zhu, Qinghua; Shen, Jie
    Source Title:IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
    Language:English
    Document Type:Article
    Keywords Plus:PERFORMANCE; SENSOR; SIGNAL
    Abstract:Magnetohydrodynamic angular rate sensor (MHD ARS) can precisely detect angular vibration information with a bandwidth of up to one kilohertz. However, due to secondary flow and viscous force, it experiences performance degradation when measuring low-frequency angular vibrations. This article presents an adaptive Kalman filter that uses online angular random walk (ARW) estimation to correct for the low-frequency error of MHD ARS, where a microelectromechanical system (MEMS) gyroscope is used to measure low-frequency vibrations. The proposed algorithm determines the signal frequency based on the ARW coefficients and adjusts the measurement noise covariance to achieve accurate fusion results. Thus, the method solves the problem of frequency-dependent variation of the amplitude response of the sensors in data fusion. Initially, the algorithm calculates the ARW coefficient recursively utilizing the measurement signals of both sensors. Then, the operational frequencies of both sensors are determined by analyzing the correlation between the ARW coefficient and frequency. Subsequently, in the Sage-Husa adaptive Kalman filter (SHAKF), the Kalman gain matrix is adjusted by modifying the measurement noise variances of both sensor signals individually. Moreover, the stability of the proposed algorithm is achieved by introducing an adaptive matrix to constrain the measurement noise covariance estimation. In the experiment, the fusion effects of single-frequency and mixed-frequency signals are tested separately. The experimental results show that for frequency variation and frequency mixing, the proposed algorithm in this study significantly improves the fusion results.
    Addresses:[Su, Yunhao; Han, Junfeng; Ma, Caiwen; Wang, Xuan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Photoelect Tracking & Measurement Technol Lab, Xian 710119, Peoples R China; [Su, Yunhao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wu, Jianming; Zhu, Qinghua; Shen, Jie] China Aerosp Sci & Technol CASC, Shanghai Acad Spaceflight Technol, Shanghai 200240, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:73
    Article Number:9509510
    DOI Link:http://dx.doi.org/10.1109/TIM.2024.3375962
    數(shù)據(jù)庫ID(收錄號):WOS:001219576300010
  • Record 369 of

    Title:Intelligent Space Object Detection Driven by Data from Space Objects
    Author Full Names:Tang, Qiang; Li, Xiangwei; Xie, Meilin; Zhen, Jialiang
    Source Title:APPLIED SCIENCES-BASEL
    Language:English
    Document Type:Article
    Abstract:With the rapid development of space programs in various countries, the number of satellites in space is rising continuously, which makes the space environment increasingly complex. In this context, it is essential to improve space object identification technology. Herein, it is proposed to perform intelligent detection of space objects by means of deep learning. To be specific, 49 authentic 3D satellite models with 16 scenarios involved are applied to generate a dataset comprising 17,942 images, including over 500 actual satellite Palatino images. Then, the five components are labeled for each satellite. Additionally, a substantial amount of annotated data is collected through semi-automatic labeling, which reduces the labor cost significantly. Finally, a total of 39,000 labels are obtained. On this dataset, RepPoint is employed to replace the 3 x 3 convolution of the ElAN backbone in YOLOv7, which leads to YOLOv7-R. According to the experimental results, the accuracy reaches 0.983 at a maximum. Compared to other algorithms, the precision of the proposed method is at least 1.9% higher. This provides an effective solution to intelligent recognition for spatial target components.
    Addresses:[Tang, Qiang; Li, Xiangwei; Xie, Meilin; Zhen, Jialiang] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Tang, Qiang; Xie, Meilin; Zhen, Jialiang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:14
    Issue:1
    Article Number:333
    DOI Link:http://dx.doi.org/10.3390/app14010333
    數(shù)據(jù)庫ID(收錄號):WOS:001139153100001
  • Record 370 of

    Title:Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-image-free phase retrieval from single-shot hologram
    Author Full Names:Tian, Xuan; Li, Runze; Peng, Tong; Xue, Yuge; Min, Junwei; Li, Xing; Bai, Chen; Yao, Baoli
    Source Title:OPTO-ELECTRONIC ADVANCES
    Language:English
    Document Type:Article
    Keywords Plus:RECONSTRUCTION; MICROSCOPY
    Abstract:Digital in-line holographic microscopy (DIHM) is a widely used interference technique for real-time reconstruction of living cells' morphological information with large space-bandwidth product and compact setup. However, the need for a larger pixel size of detector to improve imaging photosensitivity, field-of-view, and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution. Additionally, the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image. The deep learning (DL) approach has emerged as a powerful tool for phase retrieval in DIHM, effectively addressing these challenges. However, most DL-based strategies are data- driven or end-to-end net approaches, suffering from excessive data dependency and limited generalization ability. Herein, a novel multi-prior physics-enhanced neural network with pixel super-resolution (MPPN-PSR) for phase retrieval of DIHM is proposed. It encapsulates the physical model prior, sparsity prior and deep image prior in an untrained deep neural network. The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods. With the capabilities of pixel super-resolution, twin-image elimination and high-throughput jointly from a single-shot intensity measurement, the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
    Addresses:[Tian, Xuan; Li, Runze; Peng, Tong; Xue, Yuge; Min, Junwei; Li, Xing; Bai, Chen; Yao, Baoli] Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China; [Xue, Yuge; Bai, Chen; Yao, Baoli] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; State Key Laboratory of Transient Optics & Photonics; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:7
    Issue:9
    Article Number:240060
    DOI Link:http://dx.doi.org/10.29026/oea.2024.240060
    數(shù)據(jù)庫ID(收錄號):WOS:001321134300003
  • Record 371 of

    Title:Multilevel Attention Unet Segmentation Algorithm for Lung Cancer Based on CT Images
    Author Full Names:Wang, Huan; Qiu, Shi; Zhang, Benyue; Xiao, Lixuan
    Source Title:CMC-COMPUTERS MATERIALS & CONTINUA
    Language:English
    Document Type:Article
    Keywords Plus:DIAGNOSIS ALGORITHM; PULMONARY NODULES
    Abstract:Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computeraided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows: 1) Concentration on the lung parenchyma region: Based on 16 -bit CT image capturing and the luminance characteristics of lung cancer, we proffer an intercept histogram algorithm. 2) Focus on the specific locus of lung malignancy: Utilizing the spatial interrelation of lung cancer, we propose a memory -based Unet architecture and incorporate skip connections. 3) Data Imbalance: In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples, we scrutinize the existing loss function and suggest a mixed loss function. Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy, measured as Area Overlap Measure (AOM) is superior to 0.81, which markedly ameliorates in comparison with conventional algorithms, thereby facilitating physicians in diagnosis.
    Addresses:[Wang, Huan; Qiu, Shi; Zhang, Benyue; Xiao, Lixuan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Peoples R China; [Qiu, Shi] Fourth Mil Med Univ, Sch Biomed Engn, Xian, Peoples R China; [Xiao, Lixuan] Univ Illinois Urbana Champion, Champaign, IL USA
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Air Force Military Medical University
    Publication Year:2024
    Volume:78
    Issue:2
    Start Page:1569
    End Page:1589
    DOI Link:http://dx.doi.org/10.32604/cmc.2023.046821
    數(shù)據(jù)庫ID(收錄號):WOS:001199394600019
  • Record 372 of

    Title:Underwater Single-Photon Profiling Under Turbulence and High Attenuation Environment
    Author Full Names:Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Li, Xiangyu; Shi, Heng; Feng, Xubin; Su, Xiuqin
    Source Title:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
    Language:English
    Document Type:Article
    Keywords Plus:REGULARIZATION
    Abstract:Underwater single-photon imaging is challenging, as the transmitting path presents turbulence and strong backscattering noise; both facts degrade the image, thus hindering its applications in real world. However, current studies on underwater single-photon modeling have generally overlooked the potential impact of water turbulence on imaging performance. This oversight may result in an inaccurate characterization of the optical propagation process in realistic imaging environment. This letter proposed a joint denoising and deblurring method with regularization by denoising (JDD-RED) for underwater single-photon image that include the modeling of turbulence and the tailored restoration model, improving the performance by considering blurring mechanism, as well as advanced signal processing method. This method is validated on numerical experiments by employing joint deblurring and denoising tasks. Compared with the PICK-3-D algorithm, the JDD-RED reconstruction results demonstrate that more detailed information can be retained while denoising. In addition, the results show an average improvement of 1.48 dB in peak signal-to-noise ratio (PSNR) and 60% in structural similarity (SSIM), proving the superior performance of the JDD-RED algorithm.
    Addresses:[Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Li, Xiangyu; Shi, Heng; Feng, Xubin; Su, Xiuqin] Chinese Acad Sci, Key Lab Space Precis Measurement Technol, Xian 710119, Peoples R China; [Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Su, Xiuqin] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Shared Technol & Facil, Xian 710119, Peoples R China; [Wang, Jie; Su, Xiuqin] Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China; [Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Shi, Heng; Su, Xiuqin] Pilot Natl Lab Marine Sci & Technol Qingdao, Qingdao 266200, Peoples R China
    Affiliations:Chinese Academy of Sciences; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Laoshan Laboratory
    Publication Year:2024
    Volume:21
    Article Number:6501605
    DOI Link:http://dx.doi.org/10.1109/LGRS.2024.3432931
    數(shù)據(jù)庫ID(收錄號):WOS:001287339700008
高清无码免费在线观看| 少妇熟女视频一区二区三区| 国产精品免费无遮挡无码永久视频 | 成人免费网址| 日韩一道本视频| 国产精品亚洲五月天丁香| 色午夜视频| 国产SUV精品一区二区四| 久久99精品国产麻豆婷婷洗澡 | 日韩一级黄| 日本护士毛茸茸| 91在线视频免费观看| 国产黄片在线视频| 亚洲人妻| 可以免费看av的网站| 亚洲精品视频在线播放| 一级a毛片免费观看久久精品| 日逼视频免费| 欧美熟妇另类久久久久久牛牛影视 | 欧美性爱入口| 中文字幕一区在线观看| 在线亚洲精品| 国产午夜精品视频| 高清无码91| 精品久久av| 亚洲精品91| 午夜视频福利在线观看 | 顶级嫩模被啪到呻吟不断| 伊人久久一区| 2024狠狠爱| 免费AV观看| 国产免费无码一区二区| 一级二级毛片| 国产成人一区二区三区| 精品久久久久久久| 特级黄色一级片| 日本熟妇成熟毛茸茸| 国产精品久久久久久中文字| AV无码波多野结衣| 人人摸人人上人人| 国产东北女人做受av| 高清无码黄| 日韩免费视频观看| 国产精品农村无码A片| 91福利在线观看| 69久久精品无码一区二区| 九九久久久精品| 精品久久影院| 自拍偷拍一区二区三区| 强开小婷嫩苞又嫩又紧视频| 不卡一区二区在线| 2024狠狠爱| 日韩国产成人| 顶级欧美做受xxx000大乳| 无码人妻束缚av又粗又大| 77777av| 国产精品对白久久久久粗| 国产永久精品大片wwwApp| 亚洲精品不卡| 国产精品一区二区三区免费观看| 女同一区二区| 夜夜操夜夜操| 日韩无码观看| 人人爱操| 免费无码国产在线观看九色了| 欧美一级a一级a爰片免费免免| 顶级欧美做受xxx000大乳| 日韩操逼片| 丰满熟妇大号BBWBBWBBW| 日韩在线视频精品| 国产精品第四页| 五月天综合色| 91精品国产一区二区| 人人爱人人摸人人要| 国产一级A片无码免费下载樱花| 亚洲中文字幕一区| 交视频在线播放| 久久精品亚洲| 亚洲午夜精品A片91一91| 日韩人妻一区二区三区| 特黄99视频| 午夜视频免费在线观看| 真人毛片| 国产视频精品在亚洲| 95国产精品人妻无码久| 18禁黑丝| 一区高清无码| 亚洲黄在线观看| 亚洲大片在线观看| 欧美色图在线观看| 亚洲中文字幕无码AV永久| 精品少妇人妻AV一区二区三区| 99久久国产热无码精品免费| 蜜臀av成人精品蜜臀av| 亚洲天堂一区二区三区| 黄片久久| 国产精品亚洲天堂| 今晚国产乱伦av网站| 啊v在线观看视频| 亚洲精品系列| 超碰伊人| 999国产精品永久免费视频APP| 性虎精品一区二区三区| 凹凸国产熟女精品福利11| 宅男午夜影院| 亚洲中文字幕人妻| 成人高清无码视频| 日韩无码第一页| 欧美一区二区三区免费| 色欲综合在线| 在线观看中文字幕| 人人操人人早| 日韩二区在线| 一级免费黄色片| 国产中文字幕在线观看| 午夜人妻理伦影片| 日韩无码看片| 一本久久综合亚洲鲁鲁五月天| 免费看一级毛片| 午夜精品久久久久久毛片| 欧美亚洲一区| 一区二区无码av| 黄色无码网站| 亚洲无码视频一区| 亚洲黄色在线| 色婷婷在线播放| 91精品综合久久久久久五月天| 人妻无码专区| 亚洲精品无码久久久久av| 人人肏 人人摸| 亚洲熟妇无码AV| 午夜成人免费视频| 国产精品码在线观看0000| 亚洲大片免费看| 99国产精品免费视频观看8| 欧美小黄片| 秋霞AV影院| A级无码视频| 国产成人一区二区三区| 性爱黄色亚洲| 无码aⅴ精品日本无码久久| 国产精品久久久久久婷婷天堂| 又硬又爽又长又粗又大毛片 | 国产成人精品亚洲男人的天堂| 无码视频免费看| 性色AV一区二区三区| 美女网站黄| 熟女一区二区三区| 国产美女网站| 国产性爱大片| 线观看免费完整aaa| 中文字幕A片无码免费看美国十次 欧美成人一区二免费视频苍井空 黄页无码 | 国产成人精品三级麻豆| 欧美大b| 天天综合天天色| 自拍偷拍一区二区| 国产91在线拍揄自揄拍无码九色| 乱伦免费视频| 婷婷在线综合| 亚洲天堂无码| 亚洲精品久久久久久一区二区| 天天插天天干| AV在线免费观看网站| 日日操天天操夜夜操| AV不卡在线| 日韩中文在线| 国产另类自拍| 亚洲高清无码一区二区| 欧美一级特黄片| 欧美日韩色| 美女黄网站| 天堂中文字幕在线| 久久激情网| 国产无码强奸视频| 免费国产黄片| 91亚洲视频| 久久精品国产乱子伦多人第1集| 日韩欧美中文字幕在线观看| 国产精品无码不卡| 国产精品人成A片一区二区| 国产精品99精品久久免费| 91看片| 亚洲三级视频| 精产国品第一页| eeuss国产一区二区三区黑人| 日日操日日爽| 艹逼艹久肏| 奶大灬好大灬好硬灬好爽在线播放| 日韩无码视频一区二区三区| 日韩人妻精品中文字幕| 日韩高清在线观看| 天天操人人操| 久久久久久亚洲AV无码| 日韩一道本视频| 免费黄色视屏| 欧美操逼逼| 特一级黄色片| 色九九九| 爱搞在线视频| www com亚洲黄色| 国产精品99在线观看| 久久综合影院| 91麻豆精品国产91久久久久久| 国产免费观看AV| 精品国产在热久久婷婷人妻AV综| 精品人妻少妇嫩草AV无码专区| 成人性爱视频免费观看| 国产成人精品久久| 国产毛片毛片毛片毛片| 男人午夜天堂| 亚欧洲精品视频| 老熟妇仑乱一区二区av| 久久黄色网址| 伊人三区| 国产精品久久久久久久黄无码| 久久99精品久久久久婷婷| av一级在线观看| 亚洲国产激情乱伦无码| 欧美精品videossexohd| 91在线精品| 国产精品视频网站| 欧美簧片| 精品国产乱码久久久久久婷婷| 中文字幕综合网| 有没有强奸乱伦免费网站免费网站| 亚洲AV中文| 91精品国产色综合久久不卡电影| 色资源网| 超碰导航| 久久久久久久国产精品| 久久电影网| 91丝袜精品久久久久久无码人妻| 一区二区精品| 亚洲av网站| 欧美成人综合| 韩国无码在线观看| 国产精品欧美日韩| 日本国产视频| 一级做a爰片毛片| 伊人999| 欧美第二页| 亚洲精品无码一区二区四区| 玖玖成人| 亚洲天堂2014| 性国产精品| 精品一区二区久久久久久无码| 国产乱视频| 一级黄色网址| 啪啪一区二区| www毛片| 黄色大片在线观看视频| 国产熟女鲁鲁视频| 牛牛影视一区二区| 岛国片在线观看| 91少妇精拍在线播放| 国产99久久九九精品无码免费| 无码高清成人| 乱女乱妇熟女熟妇综合网站| 人人操狠狠干| 国产精品乱码| 日本有码在线观看| 国产在线精品拍揄自揄免费| 亚洲一级无码| 人人妻人人澡人人爽欧美一区双 | 国产精品午夜福利视频| 四色成人A片视频在线看| 欧美三级免费观看| 亚洲成人无码在线| 亚洲国产精品一区二区三区| 久久久噜噜噜久久中文字幕色伊伊 | 国产人妻精品无码免费| 美女黄网站| 日韩在线一区二区| 一区二区国产精品| 奇米狠狠去啦| 无码免费一区| 天堂网视频| 国产精品毛片AV| 婷婷婷月天| 一级黄片在线| 国产精品久久久久久久免费看| 亚洲Av影视网| 国产91视频网站| 国产美女精品人人做人人爽| 特一级黄色片| 凹凸视频熟女一区二区| 91丨九色丨熟女高潮| 国产真人真事一级A片| 久久综合av| 国产xxxxx| 91人妻人人做人碰人人爽九色| 国产老熟女一区二区三区| 无码秘 一区二区三区| 免费三级网站| 国产一国产一级毛片日本导航| 国产性―交―乱―色―情人| 精品福利在线| 91在线视频观看| 女人18毛片水真多18精品| 玖玖视频在线| 国产黄色在线观看| 99久久国产| 色欲AV无码精品一区二区久久| 无码人妻精品一区二区中文| 黄色一级视屏| 人妻在线视频播放| jazzjazz国产精品麻豆| 欧美专区二区| 日韩AV在线免费| 亚洲第一福利导航| 亚洲精品无码视频| 国产一级av在线| 欧美一级免费| 国产爽爽爽| 欧美成人综合| 国产无码电影在线播放| 另类欧美| 久久免费影院| 精品久久久久久| 国产精品水| 亚洲丰满少妇在线播放| 色哟呦AV永久免费| 国产精品一级片| 日韩一级黄片免费看| 日韩无码性爱视频| 天天色天天操天天| 激情乱伦视频| 天堂中文在线资源| 男人的天堂电影院| 农村毛片| 狠狠干天天操| 岛国一区| 一区二区三区免费在线观看| 久久久久久久久久久99精品无码| 人妻互换一二三区免费| 亚洲精品无码AAA在线播放| 99国产精品久久久久久久久久久| 超碰在线影院| 18成年网站| 欧洲精品无码| 久久亚洲免费视频| 黄色无码网站| 狠狠人妻久久久久久综合| 国产黄片久久| 亚洲精品无码18在线| 秋霞乱伦| 日本中文字幕有码| 亚洲精品一区二区三区99| 亚洲另类视频| 国产一级片网站| 天天操天天干天天插| 四季AV一区二区凹凸精品| 综合网久久| 日韩无码一二三四| 亚洲国产精品视频| 三级片在线观看网址| 五月天婷婷综合| 西西GOGO顶级艺术人像摄影| 黄色国产视频| 国产精品一二区| 99久久精品免费看国产免费粉嫩 | 日本伊人激情| 高清性色生活片| 韩日在线视频| 少妇交换HD中文| 亚洲免费网址| 操日本美女网站| 中文字幕国产传媒| 澳门无码| 亚洲精品无码久久久久| 亚洲一级特黄大片| 超碰亚洲| 欧美精品一区二区视频| 丁香五月v国产| 欧美日韩久久| 高清国产一区二区三区四区五区| 日韩欧美一区二区在线观看| star272在线视频| 亚洲婷婷五月| 欧美精品视频在线| 国产午夜福利| 日本色综合| 精品亚洲一区二区三区四区五区高| 在线观看日韩精品| 久久有精品| 黄频在线播放| 少妇精品放荡导航| 欧美人妻曰韩精品| 在线免费观看αV| 国产精品久久久久久久久久三级 | 美女黄片| 国产黑丝AV| 人人摸人人上人人| 日韩天天搞| 国产永久精品大片wwwApp| 美女裸体无遮挡免费视频| 91综合网| 国产精品日韩无码| 国产全肉乱妇杂乱视频| 国产一级无码Av片在线观看| 日本久久久久久久做爰片日本| 无遮挡无掩盖的网站| 久久久黄色片| 中文字幕日韩三级片| 日韩无码不卡| 丰满少妇伦精品无码专区| 无套内射在线观看| 国产成人a人亚洲精品无码| 爽灬爽灬爽灬毛及A片| 亚洲一级黄色| av中文字幕一区| 欧美日一区二区三区| 99精品无码人妻一区二区| 天堂中文字幕在线| 五月天中文字幕在线| 亚洲国产视频中文字幕| 久久99无码| 亚洲精品入口| 超碰人妻在线| 国产一国产精品一级毛片| 亚洲一区二区中文字幕| 午夜福利精品| 精品无码视频| 国产一区不卡在线| 乳色无码| 在线午夜| 亚洲欧洲无码AAA片在线观看| 麻豆导航| 娇妻被交换粗又大又硬影视| 熟妇熟女一区二区三区| 亚洲国产成人精品久久久国产成人一区| 视频精品一区二区| 欧美一区二区视频| 2024国产精品| 国产精品综合| 黄片三区| 自拍偷拍第十页| 91精品国产色综合久久不卡蜜臀| 人妻999| 99精品久久久久久人妻精品| 五十路在线| 又硬又爽又长又粗又大毛片| 亚洲精品成a人在线观看| 国产AV黄片| 高清无码成人片| 国产性爱一级| 思思久ren热| 91久久偷偷做嫩草影院| 欧美,日韩,国产精品免费观看| 精品亚洲一区二区三区| 中文无码免费视频| 久久久夜夜夜| 国内精品久久久久| 中文字幕欧美日韩| 国产精品片| 国产又色又爽又刺激在线播放| 国产精品久久久久久久久久久免费看| 国内精品视频| 亚洲综合成人网| 97超碰免费| 亚洲综合一区| 91色在线视频| 亚洲精品无码久久久| 国产丨熟女丨国产熟女| 欧美视频在线一区| 亚洲一级AV无码毛片久久精品| 亚洲国产影院| 国产成人在线播放| 亚洲图片一区二区| 99热最新| 国产精品一二三| 国产综合一区无码| 国产精品久久久国产盗摄| 日韩在线| 人人操人人摸人人爽| 国产–第1页–屁屁影院| 国产精品婷婷| 日韩三级片在线播放| 影音先锋男人av资源| 五月婷婷视频在线观看| 亚洲一区二区三区| 天天色av| 国产精品无码一区二区桃花视频| 亚洲中文字幕无码AV| 精品亚洲国产成人AV制服丝袜| 精品福利导航| 国产激情久久| 午夜久久久久久禁播电影| 亚洲无码中文字幕在线| 国产精品自在线拍| 久久国产精品一区二区 | 人人操网| 国产真实伦露脸| 亚洲资源网| 亚洲国产一区在线| 国产一区二区三区免费视频| 亚洲图片欧美视频| 欧美日韩中文| 日韩精品免费视频| 狠狠精品| AV一级片| 亚洲AV电影天堂男人的天堂 | 91无码一区二区三区| 特一级黄色片| 国产午夜福利| 五月天丁香网| 亚洲无码天堂| 国产91色| 国产一级AV黄片| 日本亚洲欧美| 天天色视频| 久色91| 国产又粗又猛又黄又爽无遮挡| 91熟女视频| 亚洲欧美日韩国产综合| 久草国产在线| 91精品免费在线观看| AV一区二区在线观看| 在线观看无码AV| 国产精品亚洲精品| 日本大香蕉在线| 国产AV黄片| 4444亚洲人成无码网在线观看 | 亚洲国产精品自拍| 久久精品国产一区二区电影| 国产精品无码一区二区三区绿巨人| 日韩成人中文字幕| 亚州Av无码| 国产有码在线观看| 国产高清亚洲无码| 天堂网无码| 黄色精品| 亚州AV综合色区无码一区 | 黄色一区二区三区| 久久99免费视频| 久久国产精品无码一级毛片| 顶级嫩模被啪到呻吟不断| 国产aⅴ日本一区二区三区武则天| 日本无码电影| 国产一级男同A片免费看| 午夜精品A片一二三区蜜臀| 精品视频在线观看99| 日韩第一区| 99re在线观看| 亚洲激情一区二区| 免费人妻无码| 欧洲操逼视频| 欧美日韩一二| 三年片观看免费观看大全| 91看片在线观看| 中文字幕精品视频| 亚洲国产精品无码久久久| 亚洲图片小说区| 国产精品久久久久无码AV蜜臀| 日韩无码一区二区| 无码精品人妻一区二区三区综合部| 久草香蕉| 高清无码在线观看视频| 午夜精品久久久久久久| 国产主播福利在线| 波多野结衣精品视频| 岛国天堂av在线| 日本三级影院| 国产三级全黄A级视频| 奇米影视第四色777| 黄色小视频在线观看| 青青草91| 丁香九月婷婷| 日韩无码一二三区| 精品无人区一区二区三区聊斋艳谭| 在线观看Av网站| 国产黄片在线免费观看| 欧美性爱专区| 久久久黄色| 国产精品久久久久野外| 乱伦av中文字幕| 91伊人| 人妻精品| 伊人影院在线观看| 一区二区三区日韩| 黄片无遮挡| 91人妻人人操| 国产毛片在线看| 欧美综合图| 国产又粗又猛视频免费| 久久精品久久久久久久| 国产乱国产乱300精品| 久久男人网| 又粗又长又大手机福利视频| 久久免费视频精品| 免费黄色网站| 91成版人在线观看入口| 日韩在线播放视频| 九草在线| 美日韩一区二区| 久久亚洲天堂| 国产 亚洲 激情 小说| 国产精品人妻人伦a62v久软件| 免费AV在线播放| 国产成人精品自拍| 妞干网视频| 中文字幕日韩三级片| 国产精品九九| 99人人操| 欧美精品一卡二卡| 成人久久大片91含羞草| 成人三级片网站| 北条麻妃在线视频| 五月天丁香久久| 日韩精品成人小说网| 国产免费www| 麻豆啪啪| 亚洲AV无码久久久久网站飞鱼| 日韩久久影院| 成人影片免费观看| 美女航空毛片在线播放| 国产精品无码一区| 日韩人妻无码视频| 亚洲天堂av无码| 一本一波多野结衣| 91视频欧美| 人人爱人人操| 91久久国产综合| 国产免费乱伦| 成人超碰| 国产精品人人做人人爽人人添| 四虎在线视频| 国产乱了高清露脸对白| 日本东京热视频| 午夜视频网站| 国产又粗又爽又黄的视频| 日韩欧美少妇| 中文字幕免费在线| 精品人妻一区二区| 人人草在线视频| 美日韩一区二区三区| 久久精品欧美一区二区三区不卡| 国产毛片欧美毛片久久久| 欧美精品亚洲精品日韩精品| 1024人妻| 91精品无码久久久久久国产软件| 一级特黄女人18毛片免费视频| av中文字幕一区| 日本少妇三级片| 日韩爱爱| 香蕉一区二区| 国产乱人伦偷精品视频免下载| 亚洲无码久久久| 91成人无码看片在线观看| 99人妻| 91色综合| 国产性爱一区二区三区| 人人摸人人爱| 丁香五月婷婷综合| 欧美色综合一区二区三区| 黄色九九视频在线观看| 日韩精品在线视频| 国产AV不卡一区二区| 精品国产成人亚洲午夜福利| 国产黄色影院| 中文人妻熟女乱又乱精品| 人人摸人人看| 欧美黄片儿| 国产男女无套免费视频| 77777av| 亚洲狼人| 四虎www| 黄色一级视频| 日韩裸体视频| aV在线无码| 国产精品一区二区免费看| 国产东北女人做受av| 久久99精品久久久久久水蜜桃| 91在线精品| 欧美中文在线| 国产1级黄片| 欧美香蕉视频| 国产精品久久久久久爽爽爽麻豆色哟哟| 9l视频自拍九色9l视频| 天天天天天天中干| 日本黄色一级| 伊人色婷婷| A级网站| 国产高清无码电影| 免费激情网站| 人人狠狠| 日韩熟女激情中文字幕| 毛片免费观看| 国产无码免费看| aa一级特黄大片| 天堂网视频| 黄页免费观看| 久久久婷婷五月亚洲国产精品| 永久免费观看成人片视频网站| 丁香无码| 免费在线成人网| 91在线视频观看| 国产电影一区| 欧美色色网| 欧美乱妇狂野欧美在线视频| 岛国免费在线观看欧美| www.尤物| 无码96| 国产三级片视频在线观看| 高清欧美性猛交xxxx黑人猛交| 中文字幕一区二区三区不卡在线 | 激情乱伦五月天| 国产精品视频网| 91精品久久| 色综合区| 手机在线看片AV| av日韩一区| 又大又粗又爽| 亚洲第一综合天堂另类专| 国产不卡在线| 无码一区精品| 亚洲综合图区| 国产老熟女一区二区三区仙踪密林| 国产成人无码视频一区二区三区| 污网站免费观看| 一级亚洲| 无码人妻一区二区三区免水牛视频| 久久国产亚洲精品五月香婷 | 三上悠亚在线一区| 亚洲精品一区二区成人影7788| 国产精品一区二区三区免费观看| 超碰99在线| 高清无码在线观看av| 动漫av无码| 成人午夜福利视频| 国产精品一级无码| 亚洲欧美日韩久久| 婷婷五月av| 91亚洲国产成人精品性色| 超碰男人的天堂| 夜夜操天天操| 国产三级在线| 精品日韩人妻一区二区三中文字幕| 欧美日韩一区二| 精品欧美性爱| 免费无码国产V片在线观看视色| 久久18| 欧美亚洲中文字幕| 性爱欧美第二区| freexxx性欧美| 久久久久国产一级毛片高清版| 天天操天天日天天射| 综合在线视频| 亚洲电影在线| 欧美不卡视频| 少妇伦子伦精品无吗| 精品少妇人妻AV一区二区| 国产男人天堂| 日韩裸体视频| 欧美亚洲国产视频| 91色在线视频| 三级黄色电影网站| 国产有码在线观看| 青青草无码视频| 高清无码免费视频| 91极品人妻| 亚洲五码在线| 欧美日韩一区二区三区不卡视频| 苍井空无码一区| 狼友视频网站| 国产激情无码| 日韩欧美一区二区三区四区五区| 日韩欧美中文字幕在线观看| 精品国产乱码久久久久久浪潮| 国产在线国偷精品免费看| 色一情一乱一伦| 91九色Porny国产探花| 91人妻无码精品蜜桃| 人人草人人摸| 国产一级黄色| 午夜精品A片一二三区蜜臀| 性生交大片免费看A| 91精品久久久久久综合五月天| 在线无码视频| 色午夜视频| 国产精品无码一区二区三区绿巨人| 久久精品伊人| 日韩激情网| 一级免费毛片| 日韩精品无码一区二区河北彩花| 日本免费久久| 亚洲国产AV片| 超碰一区| 99福利导航| 日韩精品免费在线观看| 一区二区视频| 欧美牲| 免费人人操网| 热久久91| 日韩无码电影| 国产精品久久久久永久免费看| 菠萝蜜视频在线观看| 欧美日韩黄| 国产高清视频在线免费观看| 国产色哟哟| 日本中文字幕在线观看| 国产亚洲色婷婷久久99精品91| 久草干| WWW国产亚洲精品| 伊人999| aV在线无码| 乱伦一区二区三区| 国产AAA毛片| 91丨亚洲丨国产熟女| 青青草视频在线观看| 天天操天天看| 久久久久一区二区精码AV少妇| 日韩精品免费一区二区夜夜嗨| 亚洲成人无码在线观看| 人妻中文无码| 免费在线观看国产精品| 亚洲午夜久久久久久久久红桃 | 国产日韩在线视频| 国产婷婷一区二区三区久久| 久久午夜视频| 精品福利一区| 国产精品乱码一区二区三区| 久久久精品无码一区二区三区| 国产美女裸体无遮挡免费播放网站| 丁香激情五月天| 日本一区二区高清| 国产精品酒店视频| 精品国产乱码久久久久久婷婷| 精品婷婷| 在线观看国产黄片| 亚洲免费网站| AV免费在线观| 美女航空一级毛片在线播放| 狠狠操影院| 爱看男人视频午夜日韩| 日韩一二三区| 色无码在线| 美女黄18以下禁止观看| 欧美多毛熟妇| 尤物网站在线观看| 在线观看无码| 少妇无套内谢久久久久| 国产又粗又大又爽视频| 国产高清亚洲无码| 欧美日本在线| 日韩欧美一| 日韩福利视频| 国产日产欧美一区二区| 福利视频一区二区| 日韩高清无码一区| 亚洲小说区图片区| 久久99精品国产麻豆婷婷洗澡 | 欧美视频亚洲视频| 亚洲成人精品在线| 久久国产中文| 天天日天天干天天操| 久久手机免费视频| 久久福利| 综合久久亚洲| 午夜视频在线观看免费| 欧美一级片在线免费观看| 久久亚洲一区二区三区四区| 国产情侣小视频| 日本在线观看一区二区三区| 亚洲综合色视频| av无码在线不卡| 少妇又紧又色又爽又刺激视频 | 日韩无码精品电影| 亚洲乱伦网| 日韩欧美国产视频| 国产精品网址| 精品免费国产| 影音先锋男人av资源| 婷婷国产| 亚洲小说区图片区| 久久99综合| 中文乱码字幕在线中文乱码| 精品一区二区久久久久久无码| 中文字幕在线免费| 国产精品视频无码| 永久555WWW成人免费| 久久一级| 国产三级全黄A级视频| 潮喷在线| 丰满少妇被猛烈进入| 国产伦精品一区二区三区免费迷奷 | 久久人体| 亚洲狠狠爱| 豪妇荡乳1一5潘金莲| 日本三级黄色片| 91这里只有精品| 日韩欧美精品一区| 免费18禁| 亚洲一区二区三区视频| 日韩极度色诱| 亚洲系列第一页| 中文字幕制服丝袜| 人人色人人摸人人搞| 中文无码第一页| 一级免费视频| 久久国产免费观看| 五月丁香伊人网| 亚洲日本中文字幕| 色哟哟av| 狠狠干网址| 强奸乱伦亚洲无码第一页| 91无码人妻精品一区二区蜜桃| 熟女导航| 成人超碰| 国内精品一区二区| 一级二级毛片| 99精品久久久久久人妻精品 | 亚洲婷婷五月天| 国产视频一区二区三区四区| 亚洲无吗视频| 午夜无码片在线观看影院| 毛片黄色| 亚洲欧美综合| 国产精品一区二区久久| 丁香五月婷婷在线观看| 伊人色吧| 色婷婷综合久久| 国产精品久久久久久久久久久久久四虎 | 91精品国产色综合久久不卡电影| 久久久国产精品| 国产三级在线| 91精品国产91久久久| 欧美黄片免费观看|