国产免费完整高清电视剧在线看|国产免费观看高清电视剧|国产免费观看高清电视剧在线观看|国产免费观看高清完整版在线观看没重返地球|国产免费一区二区三区四区视频|国产在线观看免费高清电视剧大全

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
国产精品51| 琪琪女色窝窝777777| 国产精品五区| 国产一区二区久久| 躁躁躁日日躁网站| av影音先锋| 操逼网站视频| 一区自拍| 亚洲激情在线视频| 亚洲午夜无码AV毛片久久| 国产刺激对白| 午夜精品久久久久久久| 亚洲成人精品l国产无码AV| 国产精品第二页| 变态av| 三级片久久| 一起操网址| 精品人妻一区| 精品国产一区二区三区性色AV| 97视频在线免费观看| 天天日天天草| 99精品无码扒开猛进自慰| 久久国产一区二区| AV无码专区| 精彩无码艹逼视频| 午夜久久无码成人免费AV麻豆婷| 国产黄色免费| 无码精品一区二区三区色欲| 亚洲激情综合| 99久久亚洲精品视香蕉蕉v| 久久久综合视频| 久久婷婷五月天| 鲁鲁狠狠狠7777一区二区| 国产免费一区二区| 成人妇女免费播放久久久| 99操逼视频| 少妇导航福利| 狠狠干狠狠爱| 国产丝袜足交| 色婷婷一区二区三区四区成人网站| 精品久久99| 国产欧美精品一区二区三区色大师 | 我想免费观看在线电影视频| 欧美一区二区在线播放| 丁香激情五月天| 久久天天躁狠狠躁夜夜AV| 精品国产乱码久久久久久果冻| 亚洲AV色香蕉一区二区三区老师| 全部孕妇孕交BBBBBB| 日韩毛片免费视频一级特黄| 96人伦影院A片在线观看| 人人插人人爱| 超碰九九| 亚洲啪啪视频| 激情综合网激情网络 | 国产精品毛片无码一区二区| 国产精品观看| 黄色av网站在线免费观看| 日韩精品一区二区三区中文在线| 亚洲午夜精品A片91一91| 久久精品视频一区| 九色人妻| 欧美性爱在线视频| 天天操网站| 亚洲激情网站| 熟女综合| 操碰视频| 一区二区三区国产精品| 九色在线观看| 色综合久久久| 国产成人一区二区三区| 一级久久| 国产精品一级无码免费播放| 久久久久女人精品毛片九一| 国产精品一区二区在线播放| 国产精品超碰| 无码人妻精品一区| 亚洲天天干| 久久无码影视| 无码国产精品一区二区高潮| 国产精品无码电影| 美女AV网站| 亚洲乱色熟女一区二区三区| 三级精品在线| 色婷婷av一区二区三区大白胸| 欧美日韩一区二区三区四区| 日本中文A片理论片在线观看| 精品无码久久久久| 巨大巨粗巨长 黑人长吊| AV不卡在线| 黑人无码| 嫩草影院一区二区| 久草国产在线| 香蕉视频污版| 午夜在线| 成人日韩无码| 少妇精品一二三区拳交| 久久精品7| 欧洲AV一区二区三区| 黄色国产在线观看| 97久久精品| 欧美特黄片| 亚洲精品一区二区成人影7788| 国产第8页| 国产成人精品一区二区三区在线| a片在线播放| 人人干黄色| 国产无码乱伦视频| 中文字幕3页| 久久电影网| 成人精品无码| 久久凸凹视频| 日韩无码色图| 噜噜噜av| 国产内射一级| 日韩黄色网站| 国产99久久久久| 一级黄色无码| 久久久人人爽爆乳A片| 无码人妻aⅴ一区二区三区91| 国产丝袜在线| 无码在线电影| av免费网站| 无码中文字幕| 国产午夜精品无码理伦片| 麻豆91视频| 亚洲成a人片7777777影片| 国产精品无码粉嫩小泬| 成人在线中文字幕| 中文字幕免费在线| 经典真实偷拍系列合集| 国产小视频在线| 日本视频久久| 2024av| 亚洲精品乱| 欧美熟妇性爱视频| 大香蕉久久久| 国产亚洲精品久久久久久牛牛 | 亚洲伦理一区二区| 成人网站在线免费观看| 国产精品久久久久久一级毛片探花| 99久久精品一区二区三区| 男人天堂一区| 无码天堂| 日本无码电影| 看毛片网站| 黄片三区| 色综合久久88色综合天天| 国产日韩欧美一区| 国产乱叫456在线| 天天爽夜夜爽夜夜爽精品视频| 高清无码在线观看av| 久久99国产综合精品免费| 国产精品黄色| 国产自拍网站| 日韩人妻视频| 狠狠干成人| 亚洲av播放| 天堂AV国产一区二区熟女人妻| 日日干日日射| 青青草伊人| 国产一区二区视频在线| 天天干天天日天天操| 欧美精品 - 色哟哟| 99re99| 国产影视久久久| 偷拍亚洲一区| 黑人巨大精品人妻一区二区| 黄色无码网站| 91老肥熟| 69av在线| 亚洲免费观看视频| 国产精品无码A∨在线播放| 日本熟妇乱伦| 又长又粗又爽美女高潮视频| 亚洲AV无码牛牛影视| 精品无码久久久久久国产牛牛影视| 福利视频一区二区| 胆小鬼电视剧在线观看完整版| 狠狠综合久久AV一区二区老牛| 伊人影视一二三区综| 国产淫乱AV| 黄色国产| 成人网站视频在线观看| 老熟妇仑乱一区二区av| AV性天堂网| 中文字幕精品在线| 国产精品成人自拍| 国产一区在线观看视频| 极品91尤物被啪到呻吟喷水| 国产白丝AV| 色午夜婷婷| 亚洲群交| 色九九九| 日韩视频中文字幕| 国产乱码| 日本一区二区三区精品| 91无码精品| 三上悠亚中文字幕| 少妇3P性爱自拍| 91亚洲精品| 婷婷九月色| 美日韩一级黄片| 日韩欧美一区二区在线| 人人操人人爱人人乐人人操人人摸| 18禁美女网站| 日韩一级无码| 制服丝袜在线视频| 在线观看一区| 人人操人人干人人摸人人色| 亚洲少妇一区二区| 91视频网址| 2014av天堂| www精品| 激情综合网欧美| 超碰福利导航| 福利二区| 婷婷久久综合| 亚洲免费天堂| 色综合色| 日本久久性爱| 欧美一区二区无码三区有限公司 | 日韩精品综合| 自拍偷拍欧美亚洲| 欧美天堂在线观看| 天天干伊人久久| 欧美视频第二页| 成人做爰A片免费看网站| 性国产精品| 天天综合天天做天天综合| 欧美一区三区| 亚洲国产精品无码一线岛国| 亚洲一区亚洲二区| 黄色中文字幕| 麻豆精品在线观看| 男女交性视频播放| 精品少妇爆乳无码av无码专区| 69堂在线| 极品尤物一区二区三区| 韩国三级bd高清中字在线观看| 91中文字幕| 不卡一区二区在线| 黄色av网站在线观看| 欧美精品1区2区| 精品无码在线| 欧美熟妇在线观看| 99久久黄色| 中国一级特黄A片免费墙放| 无码高清精品| 91精品久久综合熟女| AA黄色片| 国产激情91| 国产中文字幕在线| 午夜福利视频| 超碰99在线| 国产97视频| 午夜精品国产| 精品视频国产| 久久久久久久女国产乱让韩| 国产午夜精品一区二区三| 成人动漫在线观看| 日韩黄片观看| 亚洲天堂一区在线| 黄片免费观看视频| 日韩不卡一区| av日韩一区| 人人愛人人操| 天堂av2014| 人人操人人干人人操| 私人午夜影院| 先锋资源av| 成人性做爰aaa片免费| 国产探花av| 亚洲成人一区| 国产天天操| 麻豆精品视频| 亚洲天堂久久| 色噜噜狠狠一区| 成人高清无码在线观看| 色屁屁影院| 国产精品自拍网| 国产激情偷乱视频一区二区三区| 精品少妇人妻AV一区二区| 丁香婷婷五月| 舌尖伸入湿嫩蜜汁呻吟A片视频| 日韩熟女激情中文字幕| 日操夜操| 被操网站| 最新国产成人| 中文字幕日韩精品无码内射| 欧美A级视频| 91热在线| 日韩成人无码视频| 久久久综合视频| 中文字幕在线人妻| 色网在线观看| 美日韩一级| 亚洲乱妇老熟女爽到高潮的片| 欧美熟女乱伦| 性爱视频操| 91精品国产综合久久久久久久| 国内精品嫩模AV私拍在线观看| 在线观看AV免费| 国产极品美女高潮无套在线观看| 超碰69| 国产成人三区| 日本欧美一区二区三区| 日本久久99| 无码一二三| 爆乳熟妇无码一区爆乳熟妇| 欧美美女操逼视频| 日本不卡视频| 成人无码毛片| 亚洲香蕉视频| 91高清无码视频| 国产乱伦一区二区| 最新在线中文字幕| 久久久91人妻无码精品蜜桃| 电家庭影院午夜| 国产黄色一级片| 鲁鲁狠狠狠7777一区二区| 超碰乱伦| 激情操逼视频| 嫩草视频在线| 国产精品毛片一区视频播| 亚洲男人的天堂av| 久久91精品国产91久久跳| 亚洲毛片在线| 精品国产乱码久久久久久1区2区-亚洲| 午夜视频免费| 99在线视频免费观看| 精彩无码艹逼视频| 狠狠干狠狠爱| 日韩无码天堂| 五月综合视频| 久草视频免费在线观看| 一区二区亚洲| chinese熟女老女人hd视频| 日本无码熟妇五十路视频| 国产乱人伦精品一区二区三区| 国产又粗又黄又爽又硬的| 国产在线无码| 亚洲黄色天堂| 亲子乱V一区二区三区免费看| 亚洲欧美精品| 男插女青青影院| 国内精品久久久| 午夜一区二区三区| 国产白丝一区二区三区| 欧美精品一区二区视频| 国产精品美女久久久久AV爽| 国产午夜小视频| 无码精品视频| 成人免费毛片果冻| 搡老熟女老女人一区二区| 日韩无码天堂| 国产日韩人妻一区二区三区四| 精品网站999www| 北条麻妃满足邻居的美人妻| 黄色操日本| 在线免费观看毛片| 伊人成人电影| 精品久久久久中文字幕人妻| 高清一区二区三区| 亚洲黄色在线观看视频| 四色米奇777狠狠狠me| 天天色av| 综合成人| 国产精品久久久久久久久久东京| аⅴ资源中文在线天堂| 欧美三级午夜理伦三级中视频| 狠狠干av| 国产精品一区二区视频| 国产精品第1页| 中文字幕一区二区三区乱码| 日韩欧美一级大片| 影视先锋乱伦电影| 国一产一人一伦一精| 欧美视频一区二区三区| 亚洲无码激情| 国产精品久久久久久久9999| 日韩成人免费在线视频| 无码成人精品区一级毛片| 日本一级a v| 天堂国产精品| 国产精品人妻无码一区二区三区 | 国产免费嫩草影院| 小黄片在线看| 久久性爱视频| 最好看的中文视频最好的中文| 青青草久久久| 久久精品毛片| 免费无码国产在线观看九色了| 性久久久久久久久久久久久久| 被老头玩弄的漂亮人妻| 片库| 丁香六月| 久久影院一区| 久久天堂av| 国产视频无码| 秋霞av无码| 欧美视频三区| 四虎色播| 18禁美女| 国产精品日本| 国产精品久久久久永久免费看| 91这里只有精品| 韩国无码在线| 国产AV一区二区三区| 成人性生交大片免费看中文| 国内精品国产成人国产三级| 久久国产乱子伦精品一区二区| 国产精品国产自产拍高清av水多 | 欧美国产日韩视频| 久久亚洲综合| 国产乱论| 精品欧美| 无码人妻中文字幕| 久久国产视频网站| 一级a免一级a做免费| 日韩天天搞| 秋霞一级片| 亚洲国产网址| 日韩无码免费电影| 日韩欧美精品| 国产一区中文字幕| 精品一级黄片| 91人人爽人人爽人人精88V| 国产精品日韩在线| 欧美香蕉视频| 久久精品黄片| 国产又色又爽又刺激在线观看| 色六月婷婷| 欧洲一本二本专区在线看| 伊人色综合久久久天天蜜桃| 亚洲免费在线视频| 在线观看色| 亚洲精品一区二三区不卡| 蜜乳av免费播放| 999久久久免费精品国产| 妞干网视频| 欧美日韩国产一区| 秋霞午夜一区二区三区视频| 黄页在线观看| 黄色特级片| 夜夜操天天日| 影视先锋乱伦电影| 色悠悠在线| 99精品久久久久久中文字幕| 国产精品久久久久久亚洲调教| 国产一区2区| 中文字幕日韩精品无码内射| 怡红院视频| 欧美边做饭边被躁BD在线看 | 亚洲无码三级片| 嫩草AV无码精品一区三区| 丰满白嫩大尺度裸体尤物免费视频| 中文字幕无码人妻| 国产精品久久久久久精| 特级黄色网站| 亚洲激情在线| 暗哟交小U女国产精品袍频| 美女视频毛片| 欧美一级精品| 少妇高潮视频| 污网站在线看| AV中文在线播放| 国产性爱一区二区三区| 国产日韩成人| 极品视频在线| 一级理论片| 一块操欧美性爱| 久久精品人妻一区二区三区| 99无码| 亚洲在线视频| 天天干天天拍| 国产精品三级| 亚洲天天干| 无码流出在线播放| 国产免费久久| 亚洲美女毛片| 一级黄色片毛片| 日韩无码性爱视频| 欧美性爱十二区| 十八禁视频网站| 91偷拍一区二区三区精品| 91这里拍自| 欧美午夜精品久久久久免费视| 日韩无码成人| 中文字幕乱码亚洲精品一区| 国产一级无码Av片在线观看| 哦┅┅快┅┅用力啊熟妇在线视频| 国产无码内射| 中文精品久久久久人妻不卡无码| 国产精品久久久久久久AV超碰| 91网站在线播放| 国产成人91亚洲精品无码观看| 天天日天天爱天天操| 中文字幕日韩精品无码内射| 日韩一级视频| 一区两区小视频| 欧美日韩精品在线| 天天干夜夜一操| 天天日天天操天天射| 国产精品爆乳| www.久久| 日韩免费视频| 亚洲夜夜操| 国产精品偷伦免费观看视频| 一级AV电影| 欧美日韩系列| 777奇米第四在线精品视频| 欧美视频| 人妻无码内射| 老熟女伦一区二区三区| 国产精品91在线| 高清操逼视频| 久热中文字幕| 国产精品久久一区二区三影音先锋| 91精品无码久久久久久国产软件| 99亚洲精品| 日韩国产欧美| 亚洲熟妇乱伦| 91精品国产高清91久久久久久| 玩弄人妻少妇500系列视频| 久久电影网| 亚洲人成影院在线无码按摩店| 欧美在线一区二区| 国产精品国产三级国产在线观看| 精品无码人妻一区二区三区品| 日韩精品1| 精品少妇| AV中文字幕在线| 欧美高清HD18日本| 免费么啪视频| 午夜精品一区二区三区在线视频| 成人免费在线视频| 91www| 亚洲无码高清久久精品国产| 三级国产| 国产一区二区三区视频在线观看| 国产精品一区二区三区免费观看| 国产一区精品| 无码精品电影| 日韩无码| 久久久国产精品黄毛片| 欧美日批| 国产又黄又硬又粗| 无码人妻精品一区二区三区千菊| 国产欧美欧洲| 久热国产精品| 99国产揄拍国产精品人妻蜜| 日本午夜视频| 欧美精品一区二区三区| 国产九色| 久久不卡| 亚洲免费黄色| 一级黄片免费看| 国产精品久久久久久久久久10秀| 亚洲熟女乱综合一区二区牛牛影视| 天天做夜夜爽| 91精品人妻人人做人碰人人爽| 人妻体内射精一区二区| 一本一道波多野结衣一区二区| 久草资源在线| av自拍偷拍| 丁香五香天综合情开心站网| 五月伊人网| 视频一区二区在线| 一级特黄毛片| 中文字幕欧美日韩| 日韩美女在线| 无码喷水| 国产精品伦子伦免费视频| 狼友视频在线观看| 国产一区二区成人久久919色| 国产精品偷伦视频免费看2023 | 午夜日韩无码| 成人A视频| 国产深夜视频| 乱女乱妇熟女熟妇综合网站| 亚洲少妇性爱| 黄色性爱网| 91精品网站| 91久久久久久久久| 久久激情网| 欧洲一区二区三区| 国产成人免费| 中文字幕久久久| 免费无码一级A片大黄在线观看| 99精品免费视频| 超碰福利导航| 成人网站观看| 久久国产热视频| 婷婷精品视频| 99re国产| 久操电影| 国产Aⅴ精品| 国产电影精品一区| 国产伦精品一区二区三区妓女下载| 亚洲在线视频| 经典AV在线| 人人操人人摸人人干| 怡红院视频| 少妇xxxx| 欧美A∨无码国产精品久久粉色| 曰批全过程免费视频播放动态美图| 视频在线无码| 亚洲欧洲一区| 久久黄色大片| 国产亚洲| 国产成人无码综合亚洲AV| 国产xxxxx| 欧美精品一区二区三区久久久竹菊| 精品国产乱码久久久久夜深人妻 | 91精品国产人妻女教师| 人人爽人人操| 日日夜夜精品视频| 亚洲精品自拍| 国产强奸乱伦精品| 欧洲多毛裸体xxxxx| 久久久久国产一级毛片高清版新婚 | 国产一区二区精品久久| 亚洲性爱无码| 美女黄色免费| 婷婷开心激情网| 亚洲无码性爱| 性爱免费网站| 自拍偷拍专区| 国产欧美日韩一区二区三区| 日韩AV一卡| 国产视频手机在线| 国内自拍偷拍视频| 国产精品亚洲五月天丁香| 狠狠综合久久AV一区二区老牛| 玩弄白嫩少妇XXXXX性| 无码中文字幕在线| 欧美日韩爱爱| 日韩精品免费一区二区夜夜嗨| 久久精品一区二区| av亚洲欧洲日产国码无码苍井空 | 日日操夜夜爽| 国产成人综合| 久久综合色色| 伊人激情| 国产精品99精品久久免费| 99在线视频免费观看| 国产三级在线| 国产精品久久久久久久久免费桃花| 欧美日韩一区二区在线| 免费无码国产在线56| 男人资源站| 日本无码视频在线观看| 成人午夜福利在线观看| 91在线免费看| 久久免费影院| 乱伦视频网站| 五月丁香伊人网| 国产自慰网站| 日韩黄色片在线观看| 一级做a爰片性色毛片视频停止| 天天看天天爽| 久久国产乱| 亚洲综合图片| 男女无遮挡网站| 亚洲三级网站| 思思热视频在线观看| 人人摸人人操人人干| 国产精品无码在线播放 | 成人欧美一区| 一级毛片久久久久久久18| 爱搞在线视频| 日本久久高清| 久久久久国产一级毛片| 国产精品女同| 国产美女裸体无遮挡免费视频| 无码黄色片免费| 久久久久国产一级毛片高清版新婚| 米奇影院888一区| 午夜精品视频在线观看| 亚洲三区在线观看| 口爆吞精在线观看| 国产精品内射| 国产精品视频合集| 乱伦天堂| 精品黑人一区二区三区| 精品视频导航| 亚洲精品区| 日韩无码资源| 你懂的电影| 97精品视频| 国产精品久久毛片AV大全日韩| 日韩无码人妻| 欧美精品久久久久| 九九热在线观看| 久久精品1| 亚洲视频在线免费观看| 一级做a爰片久久毛片无码电影| 四川一级少妇A片免费| 思思热在线观看视频| 极品丰满少妇XXXHD剃毛| 亚洲精品国偷拍自产在线观看蜜桃| 亚洲精品一区二区三区成人片| 岛国精品在线播放| 日韩 精品 无码 系列 另类| 视频精品一区二区| 久久综合av| 日韩高清在线观看| 国产A视频| 免费AV在线播放| 欧美激情国产日韩精品一区18| 亚洲婷婷五月天| 亚洲特级黄片| 国产AV久剧情久久久| 国产丝袜一区二区三区免费视频| 欧美九九| 超碰免费人妻| 欧美在线一级视频| 影音先锋av在线资源| 欧美自拍视频| 亚洲天天操| 亚洲图片欧美视频| 日本午夜精品| 91久久精品国产91性色tv| 玖玖视频| 亚洲天堂一区二区三区| 亚洲免费观看视频| 91极品国产| 日本一级婬A片免费看| 在线视频午夜| 凹凸视频在线| 中文字幕在线一区| 国产精品无码一区二区三区| 一级免费毛片| 欧美一级特黄片| 激情综合网欧美| 久久综合凹凸国产一区二区三区 | 午夜福利精品| 丰满岳跪趴高撅肥臀尤物在线观看| 久久精品老司机| 秋霞午夜一区二区三区视频| 精品日韩一区二区三区| 狠狠操av| 影音先锋女人av鲁色资源久久| 免费一级毛片在线播放视频黄下载| 夜夜操夜夜操| 91福利片| 加勒比色综合| 久久欧美国产伦子伦精品按摩| 日韩无码视频专区| 国产视频久久| 成人AV一区二区三区无码金桔| 97精品人人妻人人| av日韩一区| 亚洲熟妇综合久久久久久| 久久无码影视| 国产无码在线视频| 国产精品久久久久久久久无码ⅴa 国产精品19久久久久久不卡 | 熟女久久久| 成人精品水蜜桃| 免费看黄在线观看| 日韩成人高清视频| 好屌妞视频这里只有精品| 婷婷五月天基地| 91无码一区二区三区| 伊人成人电影| 国产xxxxx| 九九自拍| 欧美日韩一区二区三区四区五区 | 国产精品久久久国产盗摄| 亚洲无码少妇| 成人三级片在线观看| 成人超碰| 日韩无码专区| 高潮喷水波多野结衣在线观看| 欧洲无码一区| 91婷婷国产欧美一区二区| 日韩无码视频专区| 久久思思欧美| 日韩视频一二三| 精品少妇视频| 日本XXX护士18一19高潮| 欧美少妇性爱| 超碰香蕉| 丁香六月激情| www.com淫荡| 五月婷婷一区二区| 国产精品毛片久久久久久久| 高清无码黄| 韩国免费一级a一片在线播放| 波多野结衣亚洲一区| 拍真实国产伦偷精品| 黄色黄片免费看| 成人久久网站| 99er热精品视频| 日本激情网站| 亚洲视频在线一区二区| 国内熟女乱伦视频| 亚洲网站视频| 日韩黄色一级片| 大香蕉国产| 97国产精品久久久| 国产精品嫩草影院com| 美女裸体无遮挡免费网站| 国产一级毛片精品A片在线美传媒| 中文字幕在线看| 无码任你操| 亚洲一区二区三区高清| 日韩性爱无码| 无码精品A∨在线观看无| A级免费视频| 久久亚洲欧美| 精国产品一区二区三区A片| 日韩欧美一级| 极品白丝 国产| 成av人片一区二区三区久久| 97精品国产| 黄片免费的| 国产又猛又黄又爽| 国产无码毛片| 女人18毛片水真多18精品| 日韩综合网| 好吊视频一区二区三区| 欧美视频| 久操国产视频| 欧美精品性爱| 一区高清无码| 2024国产精品| 东北浓毛老妇国语对白| 麻豆精品一区二区| 天天日夜夜爽| 久久99com| 国产乱来视频| 久久精品国产99精品国产亚洲性色| 日本爱爱视频| 国产日韩欧美| 玩两个丰满老熟女| 91sese| 91精品日韩| 日韩欧美偷拍| 国产精品久久久久久电影| 日日摸日日操| 日韩一级片在线观看| 日韩精品一区二区三区中文在线| 亚洲三级网| 国产精品 家庭乱伦| 秋霞在线| 9.1成人看片| 久久99久国产精品黄毛片入口| 日本一区二区三区精品| 自拍第1页| 免费黄网站| 久久久久久99| 99久久精品国产波多野结衣图片| 精彩视频一区二区| 婷婷久久久| 男人的天堂黄片| 久久精品1| 风韵丰满熟妇啪啪区老熟熟女| 久久精品欧美一区二区三区不卡| 风间由美一区二区| 日本久久高清| 一级特黄妇女高潮视的特点| 国产麻豆一区二区三区| 亚洲AV成人www新版精品久久| 调教她的尿孔(H)| 日韩无码国产精品| 丁香五月天狠狠操| 人人操人人色| 日韩综合在线观看| 久久视频在线免费观看| 日日精品| 亚洲AV无线在线观看| 亚洲精品国产一区二区三区四区在线| 欧美亚洲三级| 色综合久久av| 青青草超碰| 一快操wwwww| 一级a一级a爱片免费免免高潮| 91老熟女| 久久久久逼| 欧美bbbwbbwbbwbbw| 天天射寡妇| 国产农村妇女毛片精品久久麻豆| 色婷婷精品久久二区二区密| 美女航空毛片在线播放| av无码在线不卡| 18禁免费网站| 中文字幕在线播| 天堂在线免费视频| 欧美黄片| 日韩三级片免费看| 91在线视频| 亚洲精品三级| 亚洲精品福利| 黄色一级网站| 久久久久无码国产精品Sm高潮 | 精品少妇爆乳无码av无码专区| 久久久综合色| 国产欧美日韩一区| 日韩欧美中文字幕一区二区| 98年欧美综合性爱| 三级无码在线| 自拍偷拍av| 午夜久久久| 久久夜色撩人精品国产小说| 女同亚洲熟女女同| 国产精品无码一区二区三区免费| 国产午夜精品一区二区三区嫩草 | 精品少妇爆乳无码av无码专区| 国产精品变态另类虐交| 国产人妻777人伦精品HD| 爽灬爽灬爽灬毛及A片| 蜜桃久久久| 国产suv精品一区二区三区| 熟女无码高清裸体做爱| av天堂一区| 天天日日日| 欧美第二页| 成人毛片18女人毛片免费| 91精选国产| 日韩啪啪视频| 国产精品178页| 日韩美女一区二区三区| 亚洲国产91| 高清无码网址| 性一交一黄一片一区二区男女| 国产精品亚洲LV粉色|