亚洲精品?Ⅴ无码精品丝袜足-亚洲中文字幕在线网站-久久精品aⅴ无码中文字幕不卡-久久精品免费首页-国产高清欧美亚洲-少妇人妻精品毛片一区二区-久久国产精品亚洲艾草网-国产三级精品国产三级人妇在线-中文字幕日韩精品内射

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
久久久久99人妻一区二区三区| 91精品国产高清一区二区三区蜜臀| 亚洲AV精色AV日韩大尺度| 精品久久av| 日本无码在线观看| 天天日天天干天天操| 三级视频网站| 黄页网站免费观看| Av天堂一区二区三区| 国产特级毛片AAAAAA| 久久99日韩| 91AV视频在线| 中文字幕无码精品| 日韩免费视频一区二区| 日本熟女视频| 国产大屁股喷水视频在线观看| xxxxx国产| 国产又色又爽又刺激在线观看| 日本三级韩国三级美三级91| 久久综合伊人| 色在线观看视频| 探花一区二三区四无码| 国产午夜麻豆影院在线观看| 在线播放高清无码| 国产成人精品一区二三区| 一级黄色片视频| 91国偷自产一区二区三区老熟女 | 在线一区| 精品国产乱码久久久久久虫虫漫画 | 亚洲日本三级片| 风韵丰满熟妇啪啪区老熟熟女| 日本视频久久| 免费无码黄色| 午夜精品无码| 视频一区二区在线观看| 大香蕉国产| 91婷婷| 雯雯在工地被灌满精在线视频播放| 亚洲AV日韩AV永久无码色欲| 国产又爽又黄免费视频| 午夜福利视频免费看| 91电影在线观看| 91AAA在线观看| 漂亮人妻被强A片在线| 国内盗摄国产盗摄av| 日韩久久影院| 岛国一区二区| 欧洲精品无码一区二区三区在线| 99青青草| 高清无码小视频| 久久婷婷丁香| 国产美女裸体永久免费观看网站| 久久午夜视频| 美女黄片| 精品97人妻无码中文永久在线| 一级a做一级a做片性视频水里 | 国产伦精品一区二区三区视频我| 久久婷婷五月| 免费看成年人视频| 中文字幕一区二区三区乱码在线| 亚洲免费观看| 中文制服丝袜熟女AV亚洲| 成人H动漫精品一区二区| 九九精品在线| 亚洲欧美在线视频| 日韩 精品 无码 系列 另类| 毛片A片中文字幕在线视频| 精品无人区麻豆乱码久久久| 乱肉黄蓉合集500篇| 蜜乳在线| 欧美人伦| 成人性爱视频在线免费观看| 91美女视频在线观看| 一区二区激情| 一区二区三区高清| 国产精品一区二区三区无码| 黄网站免费观看| 日韩性爱在线观看| 91无码一区二区三区| 丁香五月天激情| 国产日批视频在线观看| 亚洲国产精品无码影视| 91一区二区三区| 99久久久国产精品无码免费| AV综合| 久久发布国产伦子伦精品| 午夜在线小视频| 亚洲制服丝袜| 亚洲无码免费观看| 福利精品在线| 国产又粗又黄又爽又硬| 日韩成人网站| 黄片免费在线视频| 国产精品日韩精品| 国产精品久久久久av| 欧美在线不卡视频| 91精品无码国产在线观看一区| 91丨中文啦丨国产九色熟女| 国产无码.con| 国产性生活视频| 欧美激情 日韩无码| AV片在线观看| 人人妻人人澡人人爽欧美一区双| 午夜一级| 麻豆精品一区二区三区av沈娜娜| 特级无码| 午夜在线观看免费视频| 国产精品久久久久久三级无码| 亚洲精品综合| 亚洲高清在线观看| 潮喷在线| 在线观看国产高清视频免费网站| 日韩成人电影在线观看 | 日韩欧美一级片| 亚洲天堂网站| 久久99亚洲精品久久99果冻| 大地资源中文在线观看官网免费| 天天躁日日躁AAAAXXXX欧美| 国产精品久久777777毛茸茸| 国产AV一级| 一区二区AV| 久久久精品电影| 亚洲精品a| 自拍偷拍第一页| 亚洲无码免费观看视频| 国产伊人久久| 亚洲欧美日韩国产综合| 天堂AV国产一区二区熟女人妻| 欧美偷伦无码一区二区| 天天综合天天| 久久久久无码精品国产sm果冻| 日韩中文在线| 综合一区| 日本国产视频| 国产精品嫩草久久久播放| mm131王雨纯极品大尺| 特级特黄AAAAAAAA片| 久久综合导航| 国产A自拍| AV在线无码| 性久久久久久久久久久久久久| 调教拨开两唇打花蒂戒尺| 久久思思热| 欧美乱码精品一区二区| 苍井そら无码av| 成人伊人| 调教妻弟的日日夜夜| 日本a在线| 日韩一级黄色| 久久精品中文字幕2345影视| 精品一区欧美| 日韩一级片在线观看| 国产精品久久久久久久久久久久久四虎| 亚洲激情小说| 久久久婷婷| 久久久久无码精品国产高潮| 毛茸茸性XXXX毛茸茸| 久久天堂| 日韩欧美一区二区三区四区五区 | 99国产在线拍91揄自揄视| 欧美亚洲性爱| 黄aaaaaaaaaaaaaaaaaa色网站| 国产a区| 亚洲精品夜夜操操| 亚洲AV永久无码国产精品久久| 色天堂影院| 亚洲视频无码| 日本性爱视频在线观看| 免费黄片在| 高清黄片| 成片免费观看视频大全| 亚洲欧洲一区二区三区| 国产三级在线观看| 欧美日韩系列| 被解救的姜戈| 久久无码电影| 亚洲性网| 久久人人爽人人爽人人片亚洲| 日本久久久久| 国产96在线| 99精品欧美一区二区三区黑人| 国产高清无码视频在线观看| 2018天天干天天操| 婷婷五月天激情网站| 免费a视频| 亚洲狠狠婷婷综合久久久久图片| 91小视频| 亚洲精品乱码久久久久久麻豆不卡| 亚洲精品国产suv一区| 欧美天堂一区| 日韩中文字幕人妻在线| 久久亚洲视频| 亚洲图片综合网| 在线视频中文字幕| 看毛片网站| 无码成人精品区一级毛片 | 国产一级视频在线观看| 噜噜噜噜人人澡夜夜天堂| 日日夜夜精品| 精品人妻一区二区三区视频53一| 啊灬啊灬啊灬快灬高潮了女| 久久性爱免费的| 超碰导航| 日本熟女网站| 亚洲中文字幕一区二区| 在线免费观看国产| 一本一道久久a久久精品综合| 免费乱伦视频| 91精品国产高清一区二区三蜜臀| 手机在线看片AV| 嫩草91影院| 视频在线一区二区三区| 日韩不卡一区| 高清无码一区| 欧美性爱视频在线播放| 天天干天天日| 伊人网视频| 怡红院成人网| 中文字幕人妻视频| 国产另类自拍| 久久四区| 黑人免费福利视频| 久热中文字幕| 日韩欧美国产亚洲| 亚洲欧洲自拍| xxxxx欧美| 久久国产精品一区| 99国产视频| 国产激情在线| 日韩精品免费一区二区夜夜嗨| 国产一区在线看| 性爱福利导航| 毛片99| 国产一级aa| 91中文字幕在线播放| 中国免费一级片| 屁屁影院第一页| 亚洲一区免费观看| 青青草华人在线| 91免费国产视频| 欧美黄片免费观看| 白嫩少妇激情无码| 国产欧美黄片| 国产精品久久久久久久9999| 天天日天天插| 欧美综合图| 91精品视频在线播放| 国产精品二区在线| 成人精品在线播放| 日本在线看| 人人操91| 亚洲有码视频在线观看| 中文字幕一区二区久久人妻网站 | 丁香五月激情综合| 久久青草视频| 日本一区不卡| 国产另类视频| 91丨九色丨蝌蚪丰满| 精品一区二区无码| 另类小说综合网| 亚洲夜夜操| 最新中文字幕在线观看| 国产黄色一级片| 久久只有精品| 亚洲一区二区三区丝袜| 亚洲精品综合| 中文字幕黄色电影| 无码人妻精品一区| 亚洲精P| 九九视频免费| 91人妻无码精品蜜桃| 久草精品在线| 五月天丁香网| 久久性爱电影网站| 麻豆精品一区二区三区| 日韩性爱无码| 亚洲精品自拍| 日韩影院黄片| 亚洲精品成人网站| 91精品国自产在线观看| 亚洲一级黄色| 三级中文字幕| 欧美色图第一页| 亚洲一级毛片| 国产色图乱伦| 午夜精品久久久久久久四虎美女版| 国产一区精品在线| 欧美日韩在线视频播放| 国产精品影视| 欧美XXXBBB| 亚洲女同一区二区| 福利视频一区二区| av资源网址| 精灵梦叶罗丽第八季| 粗大的内捧猛烈进出在线视频| 久久电影网| 久久国产精品一区| 三级片在线观看网站| 黑人巨大精品欧美一区二区免费 | 乱熟女高潮一区二区在线观看 | 免费高清无码视频| 无码少妇精品一区二区免费动态| 久久国产精品影视| 在线观看黄网站| av香蕉| 国产夫妻av| 免费一级a| 久久只有精品| 人人摸人人爱人人舔| 国产性爱AV| 欧美日韩操逼| 91蜜桃网| 日韩精品极品视频在线观看免费| 日本无码完整视频波多野结衣| 99精品国产91久久久久久无码| 欧美黄色大片| 日韩AV无码中文无码不卡电影| 国产91在线播放| 午夜精品国产| 国产三级自拍| 高清无码小视频| 一区二区三区四区在线视频| 无码成人动漫| 欧美成人性色生活片| 国产精品IGAO视频| 欧美91| 国产精品无码一区二区三区| 国产小黄片在线| 老妇高潮潮喷到猛进猛出| 国产一级精品视频| 国产真实乱对白精彩久久老熟妇女 | 亚洲无码免费观看视频| 久久久免费| 久久久久亚洲AV无码网影音先锋| 99热精品在线观看| 天天拍夜夜操| 欧美日韩有码| 麻豆啪啪| 国产午夜精品一区二区三| 粉嫩av一区二区三区天美传媒| 国产精品无码久久| 日韩欧美在线一区| 免费观看操逼视频| 99在线无码精品| 国产成人精品无码免费看点牛影视| 一级av在线| 亚州AV综合色区无码一区 | av自拍偷拍| 欧美在线不卡视频| 国产va在线观看| 国产成人一区二区三区A片免费| 人人操人人摸人人操| 久久久黄色网| 欧美日韩黄色| 精品一区二区三区电影| 天天干天天谢| 欧美三日本三级少妇三| 亚洲综合区| 国产又粗又大又爽| 国产精品大香蕉| 日韩美女一区二区三区| 热久久久| 日韩91| 精品无码成人| 天堂久久精品| 久久国产二区| 3d动漫精品一区二区三区| 激情小说图片| 亚洲天天操| 99久久精品毛片无码一区三区| 国产AV一卡二卡| 麻豆乱伦| 久久不射网| 国产无码久久| 国产精品vA| 福利午夜无码AAA片不卡夜色| 亚洲综合激情| 精品视频99| 特级特黄A片一级一片| 国产成人在线免费视频| 伦一理一级一A一片| 色一情一乱一乱一区91Av| 精品人妻一区| 欧美久久精品| 91久久精品国产91久久| 国产亲子伦视频一区二区三区| 中文字幕乱码亚洲精品一区| 秋霞无码| 亚洲国产精品久久久久久6q| 一区手机福利视频导航| 国产白嫩漂亮KTV在| 国产乱色视频91| 欧美XXXBBB| 亚洲综合无码| xxxx18一20岁hd| 午夜久久久久久禁播电影| 91AV色| 国产一区二区三区四区五区加勒比| 日韩 cbbav| 91色逼资源| 亚欧专区| 亚洲大片在线观看| 国产又粗又黄又爽又硬的| 国产精品偷伦视频免费观看了| 秋霞视频在线| 免费不卡av| 久久国产小视频| 一级特黄孕妇AAA| 欧美精品在欧美一区二区少妇| 成年免费视频黄网站在线观看| 成人毛片大全| 春色AV| 亚洲黄片在线播放| 欧美插逼视频| 免费无码淫片aaa| 被绑到房间用各种道具调教| 人妻性爱视频| 国产精品伦子伦免费视频| 色综合色综合网色综合| 毛片无码免费| 国产男人天堂| 精品乱伦一区二区三区| 日韩中文字幕一区| 亚洲av成人精品一区二区三区| 久久久久久91亚洲精品中文字幕| AV在线免费观看网站| 国产性爱一级| 69国产| 天堂一区二区| 久久午夜视频| 日韩欧美国产高清| 嫩草91影院| 91com欧美乱伦| 99久久影院| 亚洲欧美一区二区三区| 天天操人人爱| 韩国无码一区二区三区精品| 91麻豆精品秘密入口| 日韩精品第一页| 波多野结衣亚洲一区| 蜜乳视频免费网站| 亚洲制服丝袜| 亚洲高清毛片| 69堂国产成人精品视频| 久久天天操| 搡老熟女老女人一区二区| 黄色精品视频在线观看| 漂亮人妻洗澡公日日躁| 欧韩在线视频| 成人高清无码在线观看| 精品久久av| 久久综合一区| 久久国产精品无码一级毛片| 99热在线免费观看| 成人区精品一区二区婷婷| 亚洲乱伦视频| 这里只有精品在线| 日本熟女网站| 强奸乱伦_第1页_紫色AV| 91人妻人人澡人人爽人| AV在线一| 国产SUV精品一区二区883| 一级a爱大片免费视频| 精品人妻一区二区三区四区五区在 | 日韩欧美在线看| 青娱乐极品盛宴| 麻豆乱伦| 3D动漫精品啪啪一区二区免费| 99福利| 亚洲高清无专砖区| 日本操逼视频| 精品一区二区久久| 乱淫视频| 无码性生活| 成人精品水蜜桃| 俺来也夜色阁| jzzijzzij亚洲熟女少妇18| 91九色在线视频| 99精品国自产在线| 精品一区二区在线观看| 国产免费一级| 国产福利在线| 黄片在线免费观看| 一级a一级a爱片免费免会员色欲| 欧美色图一区二区三区| 久久久精品人妻| 免费AV观看| 久久国产精品一区二区| 久久综合精品国产二区无码不卡| 91久久久久久久久久久久| 日韩中文欧美| 婷婷综合在线| 高清无码二区| 欧美人妻日韩精品| 国产美女裸体永久免费| 免费无码国产在线观看观喷水| 婷婷中文字幕| 久久久久一区| 国产精品久久久一区二区| 男人午夜视频| 午夜精品A片一二三区蜜臀| 欧美成人无码A片免费一区澳门| 亚洲免费在线| 罗马帝国艳情史| 欧美黑人xxx| 国产激情视频在线播放| 成人网在线观看| 毛片99| 91网址| 2019中文无码| 久草人妻| 在线观看欧美日韩视频| 九九九国产视频| 看片网址国产福利av中文字幕| 黄色无码视频网站| 少妇被躁爽到高潮无码人狍大战| 欧美操大逼| 无码人妻久久一区二区三区免费人妻| 美女直播全婐APP免费| 爱搞在线视频| 人人摸免费视| 水蜜桃视频网站| 久久99精品久久久久久国产越南| 激情综合五月| 日韩美女一区二区三区| 国产精品无码一区二区毛片视频| 9.1成人看片| 少妇在线| 国产精品黄片| 国产00粉嫩馒头一线天91| 亚洲天堂无码av| 精品无人区一区二区三区蜜桃小说| 真实乱视频国产免费观看| 友田真希一区| 中国孕妇变态孕交XXXX| 污视频在线观看网站| 一区二区视频免费| 人妻丝袜av| 天天干,夜夜操| 欧美一区久久| 久久久久久久女国产乱让韩| 日韩精品欧美在线| 日本熟妇色| 日韩一级片在线播放| 国产一区在线午夜福利影片观看 | www.人妻| 久久这里都是精品| 男人和女人操逼网站| 国产69精品久久99不卡无限看下载| 手机在线看黄色片| 久久久久国产一区二区三区| 亚洲一级毛片| 亚洲精选在线| 最新91视频| 国产又粗又猛又大爽| 欧美日韩黄色电影| 一级片无码| 国产精品毛片久久久久久| 自拍视频一区二区| 日韩免费视频一区二区| 精品自拍视频| 国产91在线拍揄自揄拍无码九色| 九九香蕉视频| 久久亚洲欧美| 日本免费在线| 久久九九视频| 国产精品人妻无码一区二区三区牛牛| 色偷偷噜噜噜亚洲男人 | 97资源网| 午夜视频网站| 国产AV成人电影| 精品无人区麻豆乱码久久久| 久久免费无码视频| 99九九精品| 日韩精品视频一区二区三区| 国产精品一区二区欧美黑人喷潮水| 熟女一区二区三区| 日本操逼逼| 国产成人精品在线观看| 美女91| 日韩免费成人| 国产精品女主播一区二区三区 | 亚洲伊人久久综合| 国产成人Av一区二区| 熟女乱一区二区三区四区| 国产三级在线观看视频| 中文久久久| 日本一区二区三区在线视频| 97国产| 国产真实乱对白精彩久久老熟妇女 | 日逼视频免费| 一级免费黄色片| 天堂色av| 香蕉三级片| 日韩国产在线| 天天干天天操天天干| 一级片免费视频| 国产精品久久久久久吹潮| 精品国产免费无码久久久| 欧美日韩黄色电影| 色综合综合| 香蕉视频色| 亚洲乱伦色图| 秋霞伦理视频| 无码资源在线| 亚洲中文字幕乱码无码一区二区| 欧美一级全黄| 青青草原亚洲| 亚洲黑人Av| 欧美天天色| 国产AV视屏| 午夜欧美一区二区三区在线播放| 久久国产福利| 秋霞影院在线观看| 久久国产精品无码| 在线国产91| 91久久久久国产一区二区| 人妻丰满熟妇无码区免费| www精品视频| 91免费在线| 宝贝乖~腿弄大一点就不疼了| 视频精品一区二区| 中文字幕在线视频网站| 久草资源| 午夜无码日韩| 激情一区二区三区| 99人妻碰碰碰久久久久禁片| 无码aaa| 国产做a爱一级毛片久久| 蜜桃久久av无码牛牛影视| 亚洲精品一级| 福利视频一区二区| 久久瑟瑟| 国产av电影网站| 色网站在线观看| 亚洲一区二区三区四区的 | 一区二区视频在线观看| 国产精品欧美在线| 少妇又色又紧又爽又刺激视频| 人人操网| 真人毛片| 手机在线精品视频| 国产一级a免一级a看免费视频| 毛片黄色| 欧美国产一区二区三区激情无套| 手机无码| 久久99热婷婷精品一区| 秒播午夜91s| 免费黄色AV| 久久国产熟女| 99久久婷婷国产综合精品电影| 水多福利导航| 欧美性受XXXX黑人XYX性爽| 国产精品黄色在线观看| 国产精品igao视频网网址| 狂揉吃奶胸高潮视频免费| 麻豆啪啪| 欧美三级三级三级| 国产精品无码一区二区三区,| 无码操逼视| 国产亚洲精品女人久久久久久| 精品婷婷| 天堂中文av| 深夜成人视频在线| 日本久久高清| 久操伊人| 亚洲自拍色图| 久久国内精品| 亚网成色777777在线观看| 欧美天堂在线| 国产无套白浆一区二区三区| 国产一区二区视频在线观看| 亚洲欧美综合| 少妇又紧又色又爽又刺激视频| 欧美国产日韩在线观看成人| 一区精品| 亚洲小电影在线观看| 久久丫不卡人妻内射中出 | 日韩无码系列| 国产成人久久| 精品www| 国产精品嫩草影院AV蜜臀| 四虎精品激烈交乳苍井空2| 亚洲AV无码牛牛影视| 亚洲一区无码视频| 婷婷久久久| 亚洲无码视频在线观看| 无码操逼视频在线观看| 国产乱伦一区二区| 伊人激情网| 国产日本精品| 中文字幕人妻一区二区| MM1313亚洲精品无码小说| 免费无码国产精品| 黄色无码视频| 国产一级a毛一级看免费视频| 亚洲精品一| 九九成人| 蜜臀av成人精品蜜臀av| 亚洲精品夜夜操操| 每日更新AV| 国产一级A片久久久免费看快餐| 成人三级片在线观看| 五月伊人网| 国产免费一级片| 黄页网站在线观看| 99国产精品免费视频观看8| 亚洲一区在线视频| 午夜精品久久久久久| 久久综合久| 亚洲人人操| 在线观看av天堂| 黄色网址在线播放| 在线视频91| 青青操在线视频| 超碰在线中文字幕| av天堂一区| 日日操天天操| 激情欧美一区二区三区| 91精品在线观看视频| 色天堂在线| 亚洲激情小说| 色婷婷一区二区| 国产精品JIZZ久久久久久久| 无码专区AV| 色婷婷一区二区三区| 国产精品黄片| 国内精品在线播放| 日本精品视频一区二区三区| 欧美性爱区3| 岛国视频一区在线| 一区二区三区激情啪啪视频| 日韩中文欧美| 天天干夜夜爱| 国产又大又粗又猛又爽视频| 99亚洲精品| av大片在线观看| 中文天堂国产最新| 人人看人人干| 91高清无码视频| 伊人色综合久久久天天蜜桃| 国产麻豆乱伦| 唯美口活| 亚洲精品自拍| 国产精品一区二区高潮六一视频 | 国产精品毛片无码一区二区| 粉嫩av一区二区三区在线播放| 欧美人妻曰韩精品| 99er热精品视频| 国产精品一区二区久久| 黄色无码网站| 日韩无码二区| 天天干天天日天天操| 爱搞视频在线观看| 黄色三级在线观看| 国产中文在线观看| 鲁鲁狠狠狠7777一区二区| 中文乱码字幕在线中文乱码| 三级片网站在线看| 欧美专区综合| 日本无码精品| 无码不卡在线| 91大神精品视频| 亚洲美女高潮久久久| 国产成人无码不卡精品久久久| 亚洲综合一区二区| 国产女人18毛片水18精品| 日韩欧美精品在线| 宅男噜噜噜66一区二区| 精品无码黑人又粗又大又长| 人妻天天操天天干| 92国产精品| 人妻色图| 久久青草视频| 日韩无码一区二区| 91无码人妻精品一区二区| 无码人妻精品一区二区蜜桃网站| 女人高潮抽搐喷液30分钟视频 | 人妻无码| 无码不卡视频| 99国产精品久久久久久| 亚洲三级图片| 操逼无码| 伊人影视一二三区综| 国产精品久久不卡| 午夜精品久久久久久久99热浪潮| 国产综合一区无码| 色中只有这里有精品| 午夜成人网址| 午夜不卡视频| 韩国高清无码在线观看| 东京热男人的天堂| 日韩AV专区| 思思热手机在线| 香蕉视频三级片| 国产丝袜在线| 久久午夜免费视频| 欧美高清一区二区| 伊人黄色电影| 蝌蚪窝视频在线观看| 中文字幕人妻无码| 日韩中文字幕亚洲精品欧美| 天天综合天天做天天综合| 麻豆国产馆老熟妇高潮| 免费99精品国产自在在线| 久久黄色片| 黄色AA大片| 成人午夜在线| 久久亚洲视频| 精品无码久久久久久国产牛牛影视| 精品视频久久久| 熟女乱一区二区三区四区| 精品国产乱码久久久久久影片| 日韩黄色网址| 免费无码在线视频| 无人码人妻一区二区三区免费| 免费在线无码| 日韩精品一二三区| 一级毛片网址| 亚洲精品久久酒店| 国产欧美视频一区| 一本色道| 午夜黄色小视频| 囯产精品久久久久| 少妇被黑人到高潮喷出白浆| 色偷偷偷亚洲综合网另类| 玩弄白嫩少妇XXXXX性| av中文字幕一区| 国产成人在线看| 亚洲AV无码一区二区三区桃色| 最近免费中文字幕MV在线视频3| 午夜免费小视频| 日韩无码天堂| 久久久久久久久精| 亚洲精品一区二区三区2023年最新| 色欲日韩欧美亚洲| 伊人久操| 欧美一区二区精品| 久久久国产精品黄毛片| 日日碰碰| 天天躁日日躁AAAA动漫| 日本a在线| 国产白嫩护士被弄高潮| 精品久久av| 91老熟女| 伊人五月| 99视频精品在线| 91久久久| 97国产视频| 国产精品电影一区| 欧美一级片毛片免费观看视频| 亚洲九九九| 西西午夜无码大胆啪啪国模| 日韩毛片免费视频一级特黄| 日韩欧美国产视频| 毛片无码免费| 屁屁影院网站| 人妻毛片| 亚洲香蕉在线观看| 久久久国产精品| 99免费精品| 人人操天天操| 高清黄色无码| 夜夜躁狠狠躁日日躁麻豆老人| 人妻系列孕妇篇| 日本a免费| 中文字幕免费| 日韩av中文字幕在线| 成人高清无码| 国产一级黄| 亚洲大片免费看| a v最新天堂| 青娱乐91| 女人18片毛片90分钟免费| 国产在线国偷精品免费看 | 开心激情网站| 东北亲子乱子伦视频| 91色综合| 自拍偷拍第一页| 欧美一级a一级a爰片免费免免| 欧美草逼视频| 日韩一区二区中文字幕| 午夜日韩| 色就是色欧美| 极品白丝 国产| 国产粉嫩| 西西图吧| 日本无码电影| 亚洲视频在线播放| 久久人妻一区二区三区| 久久久久久网站| 亚洲无线观看| 日韩一区二区无码| 毛片无码一区二区三区A片视频| 91在线视频| 六月伊人| 黄色片网站在线观看| 国产一区在线播放| 天天日日夜夜| 全黄一级毛片免费| 日韩免费成人| 中文字幕在线免费看线人| 91手机视频在线| 国产成人精品免高潮在线观看| 欧美日韩系列| 国产96在线| 国产无码一二三区| 天天综合网在线观看| 国产一级性爱| 成人免费无码大片a毛片抽搐色欲| 国产三级片在线看| 国产老熟女伦老熟妇精品| 99视频这里有精品| 欧美簧片| 欧韩在线视频| 国产一区二区无码| 色就是色欧美| 亚洲AV不卡无码| 亚洲中文字幕一区二区| 国产一级毛片精品A片在线美传媒| 在线免费观看国产| 国产人妻777人伦精品HD| 国产精品久久久久久亚洲影视内衣| 99久99| 欧美性爱另类人妻| 人妻自拍偷拍| 免费黄色A| poronodrome极品另类|