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
亚洲精品888| 日本无码精品| 免费无遮挡男女交性视频| 懂色中文一区二区在线播放 | 99国产精品久久久久久久日本竹| 久久只有精品| 国产黄色一区二区三区| 亚洲综合国产| 午夜无码片在线观看影院| 一级毛片一级毛片| 久久午夜无码鲁丝片午夜精品| 青青青在线视频| 嘿嘿射在线| 一级黄色网| 九九久久久精品| 久久久久久91| 韩国无码一区二区三区精品| 免费毛片一区二区三区久久久 | 久久久人人爽爆乳A片| 天天燥日日燥| 久久亚洲精品成人AV| 乱伦中文| 久久久黄色大片| 国产又大又黄| 国产高清视频一区二区| 精品无码在线| 欧美A级做爰片免费看红杏出墙| 一级特黄aa大片欧美| 毛片无码免费| 91久久免费视频| 国产特级毛片AAAAAA| 日逼视频免费看| 欧美88| 岛国网站在线观看| 在线免费看黄| 久久精品人妻少妇一区二区| 日逼视频免费| 国产高清亚洲无码| 国产欧美小视频| 日韩精品久久久| 欧美另类视频| 国产黄色片在线观看| 亚洲无码视频在线观看| 国产精品无码一区二区三级不卡不 | 精品视频99| 香蕉视频精品| 久草中文在线| 国产精品久久久久久吹潮| 成人三级片在线观看| 啪啪一区二区| 日本熟女网站| 一级特黄妇女高潮视的特点| www黄在线观看| 国产骚逼| 日本不卡在线| 成人免费黄色大片| 日韩欧美二区| 青娱乐综合| 国产精品三级| 91popny丨九色丨国产| 国产精品国产三级国产aⅴ入口 | 又粗又大又爽| 国产高清无码免费| 精品亚洲AV乱码国产毛片| 人人操免费| 国产三级视频| 国产乱伦性爱| 久久久黄色片| 亚洲精品国产精品乱码不卡| 国产精品白浆一区二小说| 亚洲av网站| 香蕉久久精品| av天堂中文在线观看| 高清一区二区| 日本性爱视频在线观看| 亚洲精品高清无码| 91麻豆精品国产91久久久去除无广告| 岛国视频一区在线| www香蕉| 经典三级在线观看| 欧洲无码一区| 福利一区二区视频| 中文无码一区| 99热这里有精品| 夜夜福利| 婷婷五月天在线观看| 国产av大全| 欧美极品欧美精品欧美图片| FREEZEFRAME丰满少妇| 狠狠干网址| 国产内射一区二区| 免费人妻无码| 国产少妇| 欧美视频| 日韩免费视频| 九九成人| 亚洲 欧美 激情 小说 另类| 在线看黄色网站| 一级毛片久久久久久久18| 线观看免费完整aaa| 日韩网红少妇无码视频香港| 亚洲欧洲一区二区三区| 成人高清无码| 无码精品人妻一区二区三刘亦菲| 丁香婷婷在线| 大地资源二中文在线观看官网| 国产成人久久| 亚洲精品午夜福利| 色99热久久99热国产精品| 性无码一区二区三区| 亚洲视频一区| 胆小鬼电视剧在线观看完整版| 不卡视频一区二区| 国产色视频又粗又大在线观看| 亚洲欧洲强奸乱伦| 国产精品日本| 香蕉视频国产| 亚洲毛片| 日本无码完整视频波多野结衣| 欧美美女一区二区三区| 夜夜躁狠狠躁日日躁| 久久久久一区二区三区| 91精品国产| 国产淑女操逼| 色综合中文| 国产精品激情偷乱一区二区∴ | 一区二区三区免费在线观看 | 久久伊99综合婷婷久久伊| 手机成人在线视频| 亚洲精品自拍| 人人摸人人操| 亚洲精品一区二三区不卡| 免费无码国产精品| 亚洲高清毛片| 四虎在线视频| 亚洲国产AV片| 91蜜桃在线| 日本午夜福利| 欧美日韩在线看| 国产婷婷精品| 特级做a爰片毛片免费69| 91精品无码国产在线观看一区| 91popny丨九色丨国产| 日韩黄色网站| 五月天无码视频| 四虎无码| 91av在线免费观看| 亚洲无码校园春色| 精品无码人妻一区二区三区 | 人人看人人干| 亚洲国产精品无码久久久久久久久| 精品99久久久久成人网站免费| 99国产精品久久久久99打野战| 电家庭影院午夜| 亚洲AV无码一区二区三区性色| 小黄片在线| 日韩黄色片在线观看| 国产在线观看免费视频软件| 国产一区二区视频在线| 大美女禁视频www| 国产精品久久久久久模特| 一级做a爰片久久毛片无码电影| 国产手机视频在线观看| 操逼视频国产| 国产精品久久久久永久免费看| 激情久久五月天| 无码人妻少妇一区二区三区波多 | 美女黄色免费| 亚洲中文字幕在线视频| 久久亚洲w码s码| 美女航空毛片在线播放| 国产精品一二三区| 日韩无码电影院| 91国内自产精华天堂| 日韩无码一级片| av无码在线播放| 操逼视频无码免费看| 久久久久久av| 乱伦综合熟女| 这里只有精品在线| 日韩国产亚洲欧美| 久久久久久一区| www精品| 99在线视频精品| 黄色中文字幕| 国产乱伦第一页| 国产无码一区在线观看| 一级a免费| 国产精品黄色av| 国产一区二区免费| 久久欧美性爱| 亚洲中文字幕在线视频| 欧美偷伦无码一区二区| 国产探花av| 国产不卡在线| 国产另类自拍| 亚洲天堂一区二区三区| 淫荡网站在线观看| 日韩免费在线观看视频| 一区二区三区亚洲无码| 日韩不卡视频在线观看| 一级a做一级a做片性视频| 日本欧美一区二区| 一级a做一级a做片性视频水里 | 成人免费无码大片a毛片抽搐色欲| 国产精品无码专区| 99在线无码精品| 亚洲无码中文字幕在线| 亚洲一区二区免费看| 国产男女在线| 国产人妻精品一区二区三水牛| 日韩三级片在线| 99re国产| 国产又粗又大又爽| 日韩人妻系列| 无码中文字幕| 99亚洲精品| 欧美一区二区三区免费| 国产精品久久影视| 麻豆三级电影| 无码伊人操逼| 国产白丝AV| 久久精品国产亚洲A| 免费一区二区| 久久久精品无码一二三区| 黄色在线观看国产| 国产精品一级无码免费播放| 亚洲AV伊人久久青青草原视色| 亚欧洲精品视频| 国产精选视频| 被解救的姜戈| japanese日本丰满少妇| 欧美精品少妇| 国产精品久久影院| 凹凸精品熟女在线观看| 亚洲精品久| 国产欧美亚洲精品| 8090操逼网| 欧美日韩一区二区三区不卡视频| 国产无码精品一区| 欧美日韩精品在线| 91亚洲国产| 国产又大又黄| 午夜精品无码91| 91久久国产综合久久91精品网站 | 一区二区三区无码视频| 成人无码日韩| 麻豆系列a区二a区| 蜜臀99精品国产高清在线观看| 精品欧美一区二区中文字幕视频| 一本色道久久综合亚洲精品酒店| 国产成a人亚洲精品无码久久网| 成人欧美日韩| 四虎少妇做爰免费视频网站四| 七天探花国产精品| jlzzjlzz国产精品久久 | 亚洲夜夜操| 免费毛片网址| 亚洲国产综合在线| 国产乱码精品一区二区三区忘忧草| 精品一区二区三区在线视频| 国产又黄又粗视频| 国产三级片在线看| 国产一区在线观看视频| 熟女一区| 亚洲自拍一区| 日日干日日操| 人人搞人人干| 亚洲AV无码久久国产精品| 日韩操逼视频| 精品人妻伦一二三区久久斗罗| 中文日产幕无限码一区| 嫖老熟女x88AV| 影音先锋中文字幕资源6| 日本黄色三级片在线观看| 国产美女视频| 欧美日韩一二三| 国产网红主播AV国内精品| 97精品人人妻人人| 精品乱子伦一区二区三区火豆网| 一起操无码| 国产精品久久久久久久下载地址| 国产三级片在线视频| 日韩做a爱片久久毛片A片| 毛茸茸性XXXX毛茸茸| 欧美操逼片| 国内自拍偷拍视频| 日韩在线免费观看视频| 中文字幕三级| 久久久中文字幕| 国产又大又粗又猛又爽视频| 操逼一| 国产av成人| 国产激情一区二区三区| 97综合| 精品久久九九| 永久免费av网站| 夜夜操天天干| 五月AV| 最新中文字幕| 亚洲免费观看视频| 人人爱人人摸| 久久九九久久九九| 99精品免费久久久久久久久| 国产操比一区| 伊人久久综合视频| 亚洲免费人妻精品视频| 亚洲精品无码av牛牛影视| 亚洲精品一| 国产毛片毛片毛片| 国产精品久久久久久久久绿色| 欧美怡春院| 日韩不卡毛片| 一级外国欧美性爱黄色录像| 亚洲精品国产一区二区三区三州4点| 欧美一级黄色网| 欧美一二三四| 久久久成人网| 午夜色色视频| 天天综合永久| 女人一级毛片| 欧美一区二区三区在线观看| 久久综合伊人| 99福利视频| 国产综合精品| 黄色A一级狂操| 国产毛片在线视频| 91色视频在线观看| 精品网站999www| 又大又长又粗又硬| 高清无码成人网站| 国产精品毛片VA一区二区三区 | 内射在线| 天堂综合网久久| 中文字幕在线一区二区视频| 黄片免费在线播放| 日韩久久人妻| 日韩无码成人| 国产精品久久欧美久久一区| 日韩欧美中文| 久久久国产免费| 国产三级在线| 不卡成人| 免费观看黄色网| 嫩草影院入口一二三免费| 亚洲无码免费在线| 老熟女伦一区二区三区| 日韩精品在线视频观看| 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 | 黄色无码网站| 思思热在线观看| 日韩三级免费观看| 久久久91人妻无码精品蜜桃| 日韩精品一区二区三区在在线播放| 色九月婷婷| 国产成人精品在线| 在线视频一区二区三区| 一级毛片免费看| 伊人狼人综合| 黄片免费观看| 国产又色又爽无遮挡免费| 欧美黄片在线看| 无码不卡免费中文字幕视频| 国产人妻人伦精品1国产盗摄| 一区手机福利视频导航| 18禁网站| 国产高清视频| 超碰96在线| 91日本| 亚洲少妇性爱| 精品无人区一区二区三区软件下载| 岛国大片国产自| 在线免费观看黄| 青青草精品视频| 国产a区| 黄片AV在线| 欧美熟妇性爱视频| 奶大灬好大灬好硬灬好爽在线播放| 久久综合色色| 蜜桃久久av无码牛牛影视| 中文字幕乱伦| 婷婷在线播放| 97超碰免费| 色吧在线无码| 99人人操| 曰批全过程免费视频播放动态美图| 一区二区在线观看视频| 国产一级特黄大片视频播放| 天天草天天爽| 午夜精品久久99蜜桃的功能介绍| 精品日韩久久| 一级毛片在线播放| 91中文在线| 无码人妻少妇一区二区三区波多| 国产无码久久久| 欧美性视屏| 天天操天天操天天射| 风韵饱满的50岁老熟妇头像| 精品99久久久久成人网站免费| 国产乱伦一区| 娇妻被朋友在客厅呻吟动漫| 日日夜夜草| 男女交性视频播放| 国产精品久久久久久久| 国产色视频一区二区三区qq号| 亚洲三级久久| 欧美一级aⅴ无码毛片中文国产翁| YY111111少妇无码理论片| 少妇被躁爽到高潮无码人狍大战| 少妇超碰| 国产成人无码免费一区二区三区| 一区二区三区在线播放| 高潮毛片又色又爽免费| 久久久久中文字幕| 中文字幕国产传媒| 免费无码国产免费172| xxxx黄色| 岛国无码AV| 毛片网站免费| 亚洲AV免费在线观看| 无码天堂| 精品av| 免费操逼视频| 中文字幕在线观看视频www| 91精品免费在线观看| 69AV在线观看| 性虎精品一区二区三区| av亚欧| 国产SUV精品一区二区69| 国产一区在线播放| 国产在线精品免费aaa片| 日韩欧美熟女| 色视频一区二区三区| 九九香蕉视频| 狂野欧美性猛交免费视频| 久久久综合色| 大肉大捧一进一出好爽视频| 人妖一区二区| 精品久久影院| 日韩欧美亚洲国产| 精品国产999久久久免费| 超碰在线91| 国产自拍网站| 777婷婷天堂综合区色吧| 99免费精品| 亚欧洲精品视频在线观看| 丁香婷婷五月| 黄色a一级| 久久精品四区| 国产无码自拍| 911精品国产一区二区在线| 亚洲AV永久纯肉无码精品动漫| 欧美老熟妇一区二区三区| h片在线观看免费| AV天堂无码| 欧美日韩黄| 新久久久久久一级毛片免费看| 亚洲国产精品毛片AV不卡下载| 激情专区| 国产偷人妻精品一区二区在线| 99爱免费视频| 国产婷婷一区二区三区久久| 56pao国产成视频永久免费| 午夜无码在线观看| 亚洲欧美日韩在线| 国产黄片一区二区| 天堂网在线视频| 91丝袜视频| 一卡二卡Av| 亚洲久草| 欧美福利导航| 性爱在线播放| 日韩无码精品视频| 福利视频一区| 2023年中文字幕无码不卡| 日本久久久久久久做爰片日本| AV在线免费观看网站| 国产无码日韩| 国产成人精品久久二区二区| 日韩欧美偷拍| 日韩一区二区免费在线观看| 天天插天天操天天干| 7777kkkk成人观看| 少妇人妻一级A毛片无码| 国产精品成人免费一区久久羞羞| 欧美极品JIZZHD欧美| 北条麻妃在线视频| 自拍偷在线精品自拍偷无码专区| 黄色一级大片在线免费看国产一| 操日本美女网站| 日本精品视频一区二区三区| 国产精品九九| 青娱乐极品视觉盛宴| 老熟妇乱伦视频| 欧美无专区| 五月天伊人| 国产一级无码| 欧美一二区| 97人伦影院A片在线观看97 | 亚洲精品国偷拍自产在线观看蜜桃| 黄片无码视频| 拳交美女A片大全| 99人妻碰碰碰久久久久禁片| 欧美 日韩 亚洲 丝袜 制服| 久久精品丝袜高跟鞋| 制服诱惑一区二区三区| 日本黄a三级三级三级| 乱子轮熟睡1区| 精品国产无码在线观看| 国产美女裸体无遮挡免费视频| 制服丝袜在线视频| 九九视频免费看| 亚洲人精品午夜射精日韩| 啪啪免费| 亚洲无码一区二区在线| 无码观看操逼视频| 中文在线а天堂中文在线新版| 国产精品亚洲五月天丁香| 成年免费视频| 久久久久久久福利| 日韩久久久久久久久久| 天堂中文在线资源| 精品网站999www| 黄片无码视频| 潮喷视频在线| 91大神网址| 国产一区二区无码视频| 久久久久久伊人| 高清无码黄| 偷拍二区| 亚洲成年乱伦强奸网| 99国产精品国产免费观看| 国产a精品| 美女污网站| 欧美少妇激情| 日韩免费一区| 亚洲天堂日本| 日本有码在线观看| 又大又粗又硬的视频| 91啪啪| 欧美一级视频在线观看| 无码一区二区三区| 日韩成人免费在线| 中文字幕亚洲精品| 亚洲无码字幕| 无码任你操| 99久久影院| 99国产精品99久久久久久粉嫩| 久久精品欧美| 欧美日韩操逼| 中国无码区| 午夜在线观看免费视频| 国精品伦一区一区三区有限公司| 亚洲熟女性爱| 麻豆视频免费网站| 午夜成人网站| 亚洲图色AV| 国产免费无码av| 久久理论片| 人妻中文无码| 国产乱伦免费视频| 亚洲精品一区二三区不卡| 天天插天天操天天干| 色牛Av| 国产成人精品久久二区二区| 国产00粉嫩馒头一线天91| 一级毛片久久久| 一区二区三区日韩欧美| 黄色精品视频在线观看| 爆乳熟妇一区二区三区爆乳漫画| 拳交网| av第一区| 久久人人爽爽人人爽人人片av| 特一级一性一交一视一频| 高清无码操逼| 亚洲iv一区二区三区| 免费看一级高潮毛片2023| 五月婷婷综合网| 人人操人人| 国产第一页屁屁影院| 国产成人久久久精品| 久久久久无码| 成人A区| 天天躁AAAAXXⅹⅩ| 久久性爱视频| 亚洲综合国产| 中文字幕二区| 精品国产免费人成在线观看| av色综合| 欧美亚洲国产视频| 精品国产99久久久久久 | 香港三日本三级少妇少99| 亚洲精彩视频在线观看| 久久久一级片| 岛国片在线观看| 乱伦无码视频| 中文字幕人妻无码系列第三区| 色哟哟免费视频一区二区三区| 久久久黄色片| 国产污视频在线| 一二区无码| 一区二区视频免费观看| AV在线免费播放| 国产无码AV在线| 嫩草国产| 国产婷婷一区二区三区久久| 在线观看中文字幕| 日韩久久久久久| 日韩美女在线| 人妻在线视频| 高清无码免费看| 一级做a爰性色黄A片小优视频| 一区二区三区四区亚洲| 黄片免费的| 国产色图乱伦| 99er这里只有精品| 亚洲熟妇AV乱码在线观看| 色色色综合网| h片在线| 午夜视频在线观看免费| 91久久人澡人人添人人爽欧美| 免费观看黄色的网站| 免费二区| 久久久久久久极品内射| 国产精品久| 无码流出在线观看| 欧–美–性–交–黄–片| 天天鲁一鲁摸一摸爽一爽| 综合AV网| 精品成人| 午夜99| 人人操人人搞| 二区视频| 伦乱视频| 中文字幕精品一区二区精品绿巨人| 国产乱伦免费视频| 黄色18禁| 日日夜夜视频| 日韩午夜| 亚洲人人操| 成人性爱视频网站| 嫩草九九九精品乱码一二三| 日韩无码一区二区三区| 红桃视频一区二区无码免费| 精品人妻伦一品二品三品免费视频| 中文无码二区| 欧–美–性–交–黄–片| 久操视频在线| 偷拍亚洲一区| 黑人巨大精品人妻一区二区| 国产精品三级在线观看| 老熟女伦一区二区三区| 麻豆啪啪| 色婷婷香蕉| AV中文字幕在线| 中文字幕成人AV| 无码精品人妻一区二区三刘亦菲| 午夜精品在线观看| 精品在线一区二区| 99国产精品99久久久久久粉嫩| 96超碰在线| 亚洲精品自拍| 九九九国产| 天天操导航| 色色99| 天天操人人操| 婷婷麻豆| 久久伊人免费| 亚洲综合色网| 久久久夜色精品亚洲| 黄页无码| 黄色精品视频在线观看| 黄色av网站在线免费观看| AV电影免费在线观看| 二区三区无码| 久久久亚洲一区二区三区四区五区 | 免费无码淫片aaa| 国产人妻777人伦精品HD| 三级视频在线播放| 91久久久久久久久| 免费黄色A| 丁香激情五月天| 91久久久精品| 国产精品码在线观看0000| 亚洲欧美精品| 97人妻人人澡人人爽人人精品| 国产区在线视频| 91亚洲天堂| 国产九九九九| 一级黄片免费观看| 色视频在线观看| 在线观看操逼| 人人爱人人操| 黄色九九视频在线观看| 91精品国产一区二区| 久久久久一区二区精码AV少妇| 亚洲欧洲一区| 欧美日韩视频在线播放| 日本欧美一区二区三区| 麻豆啪啪| 秋霞无码| 久久嫩草精品久久久久| 精品不卡| 亚州AV一区二区三区| 国产精品久久久久久久成人午夜 | 日本不卡视频| 久久精品视频一区| 丁香九月婷婷| 99国产精品久久久久久久久久久 | 精品无码人妻一区二区免费蜜桃| 亚洲ⅴ国产v天堂a无码二区| 久久成人A毛片免费观看网站| 国产黄片免费| 国产污视频在线观看| 91麻豆精品91久久久久久清纯| 欧美激情黄色一级片在线播放| 无码高清免费视频| 91久久国产综合久久91精品网站| 亚洲精品无码一区二区牛牛| 日韩人妻在线视频| 欧美精品少妇| 亚洲AV色香蕉一区二区三区老师| 久久77| 一级特黄色片| 天天干视频| 视频一区欧美| 成人毛片免费| 91这里只有精品| 亚洲精品无| 成年人性爱视频免费看| 曰韩无码| AV不卡在线| 亚洲一区av| 午夜AV电影| 国产性爱在线视频| 日本在线观看视频| 被绑到房间用各种道具调教| 久久精品丝袜高跟鞋| 黄色国产视频| 亚洲无码免费网站| 国产免费A片在线观看不快色| 国产在线精品拍揄自揄免费| 色综合天天综合网国产成人网| 日韩av男人天堂| 一区二区免费看| 亚洲高清无码专区| 久久久免费观看| 精品亚洲国产成人AV制服丝袜| 99久久久无码国产精品无卡| 黄色三级网站| 三级黄片免费看| 日韩欧美爱爱| 一区二区三区无码按摩精电影| AV在线免费观看网站| 人人操天天操| 丰满少妇被猛烈高清播放| 女人18毛片水真多18精品| 视频在线一区| 国产美女裸体无遮挡,永久免费| 日韩精品无码一区二区三区久久久| 国产农村高清无套内谢视频| 一本大道无码| 超碰美女| 国产激情91| 上国产操逼网| 久久久久久久久久久国产精品| 91福利导航| 黄网在线| 一级黄片在线免费观看| 爽灬爽灬爽灬毛及A片| 一级毛片久久久久久久18| 久久久久久亚洲| 操逼30分钟小视频| 免费无码国产在线观看观喷水| A片黄色| 91五月天| 亚洲综合色网| 爱爱综合| 尤物视频在线观看| 日韩无码高清视频| 日韩在线视频免费观看| 91一级毛片| 国产视频一区在线观看| 久久永久视频| 国产日本欧美一区二区| 亚欧激情乱码久久久久久久久| 亚洲二区在线| 国产3级片| 一级特黄aa大片欧美| 欧美日韩一级黄片| 91精品国产高清91久久久久久| 性无码专区| 日本高清久久| 亚洲在线视频| 一级av免费在线观看| 国产无码手机在线| 国产精品婷婷| 国产精品原创| 亚洲高清无码在线观看| 在线免费黄片| 成人无码片免费178www| 中文字幕无码一区二区免费久久| 人妻AV导航| 中文字幕第一区| AV鲁丝一区鲁丝二区鲁丝三区| 啪啪免费视频| 国内一级毛片| 亚洲一区电影| 永久精品| 欧美多毛熟妇| 午夜丰满极品美女A片| 欧美性爱一区二区电影| 国产熟女真实乱精品91 | 色婷婷精品| 色综合区| 国产伦精品一区二区三区视频金莲| 亚洲国产精品成人综合久久久| 国产一区二区三区视频在线观看| 一区二区人妻| 国产青青操| 思思热在线| 艹逼艹久肏| 国产精品igao视频网网址| av在线一区二区| 精品一区中文字幕| 日韩无码人妻| 一级毛片视频免费看| 亚洲喷水无码一区丰满爆乳少妇| 亚洲视频在线观看| AV中文在线播放| 一区二区亚洲| 欧美呦呦| 精品一区二区在线观看| 曰韩性爱在现视屏| 亚洲女人被黑人巨大进入| 国产精品无码aⅴ嫩草| 亚洲AV无码国产精品电影三绞| 天堂中文在线视频| 国产AV综合| 五月婷婷在线观看视频| 99国产精品99久久久久久粉嫩| 色色视频区| 一级特黄毛片| 99热免费观看| 日本综合久久| 国产综合自拍| 日韩经典在线| 久久精品四区| 精品婷婷| 免费看黄网址| 少妇又紧又色又爽又刺激视频| 亚洲天堂av无码| 亚洲AV日韩AV永久无码网站| 国产性av| 91福利视频导航| 超碰在线免费| 国产日本精品| 国产精品久久久久久久久一区二区三区| 91久久九色| 2017日本三级| 欧美日韩性| 99久久99| 波多野结衣一区二区三区| 久久99精品久久久久久噜噜| 欧美熟妇色| 岛国一区| 久久中文无码| 亚洲精品午夜| 懂色av蜜臀av粉嫩av分享吧| 少妇人妻偷人精品无码视频新浪| 无码毛片免费看| 无码窝AV| 伊人激情网络| 久久成人一区二区| 欧美呦呦| 国产成人无码视频一区二区三区| 精品久久久久久久| 人妻 丝袜美腿 中文字幕| 三级片一区二区| 欧美激情区| 一区二区无码av| 国产熟妇久久777777| 99无码人妻| 国产午夜精品一区| 中文无码二区| 欧美三级片在线观看| 亚洲AV日韩AV永久无码网站 | 国产精品99久久久久久久久| 国产+日韩+国产| 国产av电影网站| japanese老熟妇乱子伦视频| 久久精品国产亚洲av瑜伽仙踪林 | 欧美三日本三级少妇三| 午夜精品久久99蜜桃的功能介绍| 狠狠的caoa| 性爱视频A| 右手影院亚洲欧美| 七七久久| 日韩黄色电影网站| 久久久婷婷五月亚洲国产精品| 好吊视频| 国产黑丝一区二区| 产国传媒91一区久久无码| 欧美爆乳一区二区| 日本中文字幕在线播放| 天天日天天搞| 国产主播喷水| 五月天狠狠爱| 无码一级| 在线观看AV免费| 欧美一级全黄| 日韩乱伦小说| 3P 内射 在线| 91视频精品| 小黄片高清| 亚洲精品入口| 在线观看亚洲AV| 亚洲永久免费| 荫蒂添的好舒服视频囗交| 欧美极品欧美精品欧美图片| 国产精品偷伦免费观看视频| 丁香五月天AV| 国产91精品久久久久久久网曝门| 午夜国产精品视频| 妞干网视频| 性做久久久久久久久| 夜夜操天天操| 五月婷婷丁香六月|