后入欧美美女在线视频|?v在观线观看男人的天堂|国产美女高潮一区视频|久久精品国产av久|中日韩精品激情在线观看网站|国产高清在线在线视频|欧美成人午夜大片在线观看|欧美乱码一区二区三区在线

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
中文无码熟妇人妻AV在线| 91无码人妻精品一区二区 | 无码做爰内谢免费视频软件| 人妻中文字幕一区二区三区| 国产精品视频免费观看| 天天久久综合| 欧美一区二区三区四区在线观看| 日本免费视频| 亚洲欧洲无码AAA片在线观看| 五月综合在线| 亚洲熟妇XXXXX| 亚洲日本天堂| 日本免费久久| 99久久免费看精品国产一区| 变态另类zoz0另类| 亚洲国产精品无码观看久久| 欧美特一级| 亚洲av播放| 亚洲中文字幕乱码无码一区二区| 国产特黄无码A片免费看| 欧美一级内射美妇网站| 我跟闺蜜公交车被弄到高潮| 日韩欧美视频在线| 亚洲黄色在线观看视频| 少妇伦子伦精品无吗| 思思久久主页| 久久久国产视频| 色欲狠狠躁天天躁无码中文字幕| 欧美一区二区在线视频| 久久精品国产一区二区电影| 伊人日本| 亚洲熟女乱综合一区二区三区| 日韩性爱免费网| 欧美操屄视频| 亚洲免费观看视频| 欧美三级久久| 8050午夜一级毛片久久亚洲欧| 欧美一级大黄片| 久久国产亚洲精品五月香婷| 97国产色呦呦呦夜嗨嗨| 欧美亚洲性爱| www.yeye操| 日韩有码在线观看| 福利精品| 乱精品一区字幕二区| 无码精品一区二区三区四区色| 国产成人无码AV| 亚洲国产福利| 国产三级精品三级在线观看| 国产天天综合| 精品人妻久久| 人妻互换一二三区免费| 国产原创精品| 国产精品99久久久久久久久| 疯狂操逼亚洲| 最新av网址| 高清AV在线| 91电影在线观看| 成人H动漫精品一区二区| 狠狠爱69AV| 亚洲AV大片| 亚州AV综合色区无码一区| 凹凸精品熟女在线观看| 伊人激情综合色| 白洁少妇一区二区麻豆| 亚洲精品久久无码77777 | 亚洲精品888| 一级毛片黄色| 欧美性爱 日韩精品| 成人电影在线播放| 娇妻被交换粗又大又硬影视 | 国产人伦A片免费高清| 色综合天天综合网国产成人网| 亚洲AV在线观看| www欧美| 亚洲高清一区二区三区| 日本人妻丰满熟妇久久久久久 | 国产亚洲精久久久久久无码色戒| 我和亲妺妺乱的性视频| 国产真实乱了老女人视频| 一本一道久久a久久精品综合蜜臀| 亚洲精品在线观看视频| 日本爆乳一区二区三区| 一级性爱毛片| 毛片无码免费| 日韩无码第二页| 乱熟女高潮一区二区在线观看| 国产在线91| 综合激情久久| 玩弄牲欲强老熟女tp121cc| 亚洲AV片无码久久五月| 99在线视频观看| 亚洲无码视频一区| 青青草视频在线观看| 青青草原亚洲| 色色婷婷五月天| jzzijzzij亚洲熟女少妇| 三年片在线观看大全中国| 日韩美女网站| 精品人伦一区二区色婷婷| 在线播放高清无码| 中文字幕 乱伦| 欧美老熟妇操姦视频| 伊人婷婷五月天| 人人操天天日| 人妻精品久久无码专区一区二区| 毛片99| 日本熟妇色| 亚洲乱伦AV| 国产无码精品视频| 中文字幕在线无码| 国产免费一级| 亚洲AV无码国产精品久久不卡嫖娼| 久久久久久网站| 欧美视频在线免费观看| 色了吧综合网| 成人超碰| 顶级嫩模被啪到呻吟不断| 欧美一级黄色大片| 黄色免费无码视频网站| 在线观看无码视频| 国产资源在线观看| 调教妻弟的日日夜夜| 日本高清视频一区| 亚洲中文字幕精品| 一级做a爰片毛片| 国产精品无码av| 一级性爱视频免费观看| 亚洲无码aaa| 黄片AV| 一区二区三区亚洲无码| 天天干天天曰| 亚洲精品www| 亚洲无码一区二区av| 日韩精品久久久| 色黄大色黄女片免费看直播| 在线免费看av| 免费操b视频| 国产精品国产三级国产aⅴ入口 | 99国产精品免费视频观看8| 国产伦精品一区二区| 在线看黄色网站| 五月社区| 久久偷拍视频| 午夜情深深| 国内自拍真实伦在线观看| 狠狠干av| av小网站| 天天日天天干天天操天天射| 成人无码片免费178www| a一级性爱啊视频在线免费看| 国产拳交HD在线| 亚洲无码mv| 特黄AAAAAAA片免费视频| 欧美α片在线播放| 黄色精品| 人人爱人人摸人人要| 国产成人在线视频| 一级a一级a爰片免免免下载| 91人人| 美女色色视频网站| 免费看一级一级人妻片| 大陆毛片| 操逼网站视频| 中文字幕一二三四亚洲日韩| 免费看黄色一级片| 热久久最新地址| 天堂8在线| AV天堂久久| 秋霞一区二区| 国产精品偷伦视频免费观看的| 国产精品内射婷婷一级二| 久久天堂网| 日本爱爱视频| 国产无码中文字幕| 人禽杂交18禁网站免费| 国产精品久久久久国产A级| 国产欧美一区二区三区不卡高清| 久久亚洲欧美| 日韩av综合| 在线观看视频一区| 无码电影在线看| 天天干夜夜艹| av无码aV天天aV天天爽| 五月天天天操| 午夜男人的天堂| 久久精品人妻一区二区三区| 在线免费观看黄网站| 国产伦精品一区二区三区妓女| av电影无码| 亚洲乱码一区二区三区| 精品久久久99| 人妻精品中文字幕无码毛片| 99国产精品视频免费观看一公开| 久久久久久三级片| 久久无码电影| 免费A级黄片| 最美情侣免费观看视频芒果TV| 91在线视频观看| 欧美碰碰| 欧美成人性爱视频在线观看| 欧美熟妇另类久久久久久牛牛影视| 成人7777| 久久无码AV| 国产成人在线免费视频| 成人午夜在线| 久久五月综合| 黑人AV无码| 色综合天天| 在线国产91| 国产精品18久久久| 国产精品日韩无码| 四虎在线视频| 免费看的av| 欧美日韩一| 韩国精品久久久| 日日夜夜草| 看毛片网址| 免费AV在线播放| 少妇粉嫩小泬喷水视频WWW| 欧美日韩一二| 白浆一区| 日韩成人无码| 中文字幕精品视频在线观看| 污视频在线看| 少妇被粗大猛烈进出免费视频| 久久日韩精品无码一区波多野| 国产福利小视频| 熟女一区二区三区四区| 久久久影院| 免费看黄色一级片| 人妻少妇中文字幕| 99精品免费久久久久久久久日本| 国产精品人妻人伦a62v久软件| 在线日韩视频| 韩国三级中文字幕HD久久精品 | 色婷婷九月天天综合| 日日夜夜天天| 青草视频在线| 91亚洲精品| 久久99国产精品黄毛片禁果| 日韩性爱在线观看| 中文无码电影| 亚洲视频免费| 18禁无码毛片精品久久久久久| 中文无码免费视频| 高清无码视频在线观看| 亚洲精品一二三| 欧美精品第一区| 影音先锋男人av| 国产91色在线观看| 五月天久久久| 99久久婷婷国产综合精品青牛牛| 高清无码一二三区| 97色色网| 三年片在线观看免费大全爱奇艺| 国产欧美精品一区| 啪啪免费网站| 成人午夜sm精品久久久久久久| 亚洲国产成人精品女人久久久| 成年人免费视频网站| 亚洲va韩国va欧美va精品| 无码人妻丰满熟妇精品区| 色播五月丁香| 亚洲AA| 国产精品99精品久久免费| 一级毛片视频免费看 | 欧美日本韩国一区二区| 黄色无码在线观看| 久久久久国产一级毛片| 日韩三级片在线播放| 亚洲av一二区| 一级黄色片在线观察| 日韩乱伦中文字幕| 另类人妖| 亚洲精品毛片| 天堂在线一区| 国产毛多水多做爰爽爽爽| 日韩成人中文字幕| 国产AV一级| 色色国产| 精品自拍AV| 欧美日韩高清丝袜| 国产AV天堂| 欧美黑人少妇高潮喷水| 日日干狠狠干| 色色色影院| 最新国产精品网站| 大粗鳮巴久久久久久久久| 丁香久久| 国产一级特黄视频| 欧美亚洲一区| 日韩精品中文字幕一区| 亚洲视频免费观看| www.国产精品视频| 国产天天操| 性免费视频| 久久精品视频8| 熟女中文字幕| 国产好爽又高潮了毛片91| 伊人色色| 国产三级片一区二区| 亚洲综合小说| 国产高清在线| 日韩欧美久久久| 成人大香蕉| 91九色在线视频| 四虎黄片| 人人操人人看人人摸| 日韩久久久久久| 大香蕉国产在线视频| 一级片在线免费观看| 四虎精品激烈交乳苍井空2| 99re在线观看| 操之久久| AAA在线观看| 男人天堂一区| 国产又粗又猛又黄| 亚洲Av无码午夜国产精品色软件| 中国AV在线| 九九热在线观看| 免费毛片网址| 欧美少妇激情| 国产精品水| 美女喷水视频| 91福利免费| 超碰香蕉| 国产无码精品电影| 强奸乱伦亚洲综合| 日韩AV无码电影| 爱搞在线视频| 中文字幕熟女人妻偷伦天美| 色七影院| 乱精品一区字幕二区| 日韩在线| 国产精品无码一区二区aⅴ污美国| 欧美一级特黄大片色| 久久久精品一区二区| 欧美日韩一卡二卡| 被老头玩弄的漂亮人妻| 色天堂在线观看| 无码一级毛片一区二区视频孕妇| 六月伊人| 黄片国产精品| 青青草成人网| 成人免费电影网站| 亚洲激情一区| 欧美三级片免费看| 九九视频精品在线| 成人在线视频app| 黄片久久| 日日干日日操| 久久高清内射无套| 日韩一区二| 午夜精品99久久久久传媒| 99精品在线观看| 欧美88| 熟女一区二区三区四区| 美女爆乳18禁www久久久久久| 男人天堂网站| 99re在线| 一级毛片久久久久久久女人18| 亚洲精品乱码久久久久久久| 欧美日韩A| 中文字幕在线观看一区二区三区 | 精品国产亚洲AV| 五月天婷婷丁香| 色xxxx| 人人操人人草人人操人人看| 一级a一级a爰片免费啪啪女女| 99re国产| 丰满熟妇大号BBWBBWBBW| 国产AV国产精品无套内谢下载| 国产成人精品亚洲日本在线观看 | 国产黄色片在线观看| 丝袜制服大香蕉| 欧美熟妇另类久久久久久牛牛影视| 国产精品永久免费视频| 日韩精品无码免费| 久久国产小视频| 国产精品无码久久久久一区二区| 国产精品久久久久久久下载地址 | 日韩成人在线观看| 一区二区三区在线视频观看| 国产精品久久久久久白浆| 国产亚洲精品久久久久久牛牛 | 无码人妻aⅴ一区二区三区69堂| 伊人网伊人网| 亚洲三级图片| 动漫精品无码| 欧美性爱一区二区| 91尤物在线| 日本黄色高清视频| 亚洲精品动漫| 国产一区二区精品无码| 91麻豆精品秘密入口| 国产aaaa| 久久久精| 国产精品小电影| 免费亚洲婷婷| 日本一区免费| 日韩毛片无码| 国产口爆| 日日夜夜视频| 欧美成人一区二免费视频苍井空| 最新国产乱伦| 一级a一级a爰片免费啪啪女女| 欧美性爱乱伦| 中文字幕亚洲乱码熟女1区2区| 国产欧美黄片| 在线无码不卡| 久久久久女人精品毛片九一| 免费激情网站| 国产成a人亚洲精品无码久久| 亚洲精品一| 成人区精品一区二区婷婷| 色天天综合久久久久综合片| 一区二区日韩无码| 国产乱码精品1区2区3区| 一区中文字幕| 欧美日韩一二三| 成人在线观看网站| 无码社区| 久久思思欧美| 乱伦av中文字幕| 亚洲熟女乱色一区二区三区久久久| 久久精品8| 成人H动漫精品一区二区| 国产精品日韩无码| 欧美日韩久久| 思思网站| 天天毛片| 天堂网在线视频| av中文字幕一区| 婷婷丁香激情五月天| 国产精品久久久久无码AV八戒| 日韩黄片勉费动态| 国产精品操| 国产精品无码电影| 中文字字幕在线中文| 国产按摩一区二区三区| 久久成人视频| 欧美一区二区精品| 国产三级探花日韩| 码精品一区二区三区四区| 免费毛片视频| 欧美操逼逼| 久久成人精品| 乱伦精品| 天天射天天操天天干| 亚洲综合色网| 美女污污网站| YY111111少妇无码理论片| 人人摸人人看| 东北亲子乱子伦视频| 精品国产乱码久久久久久虫虫漫画| 一插菊花综合网| 91精品在线播放| 国产69精品久久99不卡无限看下载| 5566成人精品视频免费| 亚洲日本在线观看| 亚洲熟妇色| 国产不卡视频一区二区三区 | 国产主播福利| 国产亚洲精品合集久久久久| 欧美精品久久久久| 一夜强开两女花苞| 精品欧美一区二区三区免费观看| 精品福利导航| 我不卡影院| 欧美三级片视频在线观看| 久久精品—区二区三区舞蹈| 国产黄在么线| 精品国产91久久久久久久黄无码 | 91无码一区二区三区| 国产乱伦一区二区| 91n免费处女在线破视频| c逼网站| 精品国产一区二区三区性色AV| 秋霞一区| 尤物网在线| 青青草原成人| 日韩精品无码久久久久成人| 国产精品日本| 亚洲男人的天堂av| 亚洲欧美国产一区二区| 精品久久久久久久久久| A级无码| 国产精品人| 国产一区二区视频在线观看| 成人免费毛片足控| 中字幕人妻一区二区三区| 青青操影院| 久久精品综合| 亚洲男人天堂AV| 在线无码播放| 高清欧美精品XXXXX在线看| AV无码专区亚洲AV毛片不卡| 在线观看欧美日韩视频| 日韩肏逼| 国产日产欧美一区二区| 玖玖综合九九在线看| 这里只有精品视频在线| 欧美性爱自拍视频| 精久久久久久| 中文字幕一区二区三区麻豆木下凛| 久久av无码| 在线二区| 无码爱爱| 成人做爰免费A片视频二机片| 无码视频二区| 91精品久久久久久综合五月天| 欧洲免费视频| 久久国产综合| 高清av无码| 亚洲精品伊人| 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 | 一级片无码| 日韩无码视频专区| 亚洲综合区| 日本欧美一区二区三区| 91人妻在线| 四川熟女大白屁股91爽| 亚洲无码一区二区av| 黄色网在线| 国产精品一区二区免费看| 五月婷婷综合网| 999久久久| h片在线看| 黄网站免费观看| 久久精品视频6| 少妇3P性爱自拍| 扒开双腿猛进入的视频免费| 国产二区视频| 黄色a视频| 欧美色色视频| 岛国大片在线观看| 国产毛片久久久久| 亚州中文字幕一区二区三区在线视频| 午夜男人天堂| 在线观看视频一区| 亚洲有码视频在线观看| 污视频在线| 伊人欧美| 国产成人精品一区二区| 亚洲欧洲强奸乱伦| 免费AV在线网址| 久久久婷婷五月亚洲国产精品| 中文欧美日韩| 日韩无码天堂| 天天燥日日燥| 日韩视频一二三| 久久久久久三级片| 懂色aⅴ精品一区二区三区蜜月 | av一起看香蕉| 国产午夜精品一区| 日本久久高清| 国产a区| 亚洲一区二区自拍| 国产一区二区久久| 一级特黄aa大片欧美| 久久精品欧美| 亚洲天堂一区二区| 乱色精品无码一区二区国产盗| 黄色成人在线| 国产粗语刺激对白性视频| 久久久久无码精品国产91福利| 亚洲欧洲强奸乱伦| 蝌蚪窉成人精品视频| 欧美黄片在线免费看| 一牛影视无码| 激情乱伦五月天| 老熟妇仑乱一区二区av| 苍井空无码一区二区三区| 国产裸体美女视频| 无码一区二区在线观看| 91国在线| 国产激情| 国产精品久久久久久亚洲色欲| 精品欧美| 熟女91| 777婷婷天堂综合区色吧| 影音先锋男人av| 国产成人无码专区| 翔田千里av一区二区| 全黄一级毛片免费| 久草中文在线| 欧美日韩视频在线| 四色米奇777狠狠狠me| 亚洲天堂| 乱伦熟女肉妇| 一级黄色片毛片| 偷国产乱人伦偷精品视频 | 可以看av的网站| 五月婷婷国产| 操逼无码免费视频| 国产小视频91| 看免费黄片| 一区二区三区日韩| 亚洲av无码一区二区三| 久久免费一级片| 国产高清不卡| 国产伦精品一区二区三区免费迷| 国产吃奶A片一区二区| 嫩草AV无码精品一区三区| 国产精品无码一区二区aⅴ污美国| 加勒比一区| 久久久久国产精品| 国产精品久久久久久久久晋中| 亚洲天堂色| 无码人妻精品一区二区三区777| 国产成人无码一区二区在线观看| 99国产视频| 91视频国产精品| 91popn.com在线生产| 黄色一级网址| 国产一级a一级a免费视频 | 黄片不用下载免费在线观看| 国产精品久久久久永久免费观看| 99热国产在线观看| 操她视频网站入口| 中文字幕在线免费看线人| 99福利视频| 国产性爱乱伦网站| 成人高清无码在线观看| 国产女人18毛片水18精品| av第一区| 亚洲精品在线观看视频| 欧美日韩一级黄片| 在线播放无码| 北条麻妃视频在线观看| 美女网站免费黄| 操逼视频免费看| 欧美视频| 欧美日韩一区二区三区不卡视频| 人成在线免费视频| 91视频播放| 激情久久五月天| free性丰满69性欧美| 在线免费看黄片| 日韩黄片免费在线观看| 看一级黄色片| 日韩中文亚洲第一| 综合无码| 人人愛人人操| 女同一区二区| 黄网站在线观看| 无码aaa| 日日夜夜精品| 99草在线视频| 欧美日韩免费| 国产毛片在线| 亚洲欧美在线一区| 午夜成人AV| 国产嫩草一区二区三区在线观看| 色就是色欧美| AV天堂无码| 国产成人精品无码免费播放精品 | 99青青草| 无码人妻精品一区二区二秋霞影院| 99re视频| 久久精品无码一区| 日本一区二区在线| 中文无码日韩欧| 国产精品久久影视| 超碰人人人| 天天干伊人久久| 国产无套内谢国语对白| 国产情侣小视频| 中文字幕av在线观看| 乱伦熟妇| 国产手机视频在线观看| 久久一级片| 欧美性爱男人天堂| 人妻中文字幕在线| 九草在线| 国产情侣小视频| 一本一道久久a久久精品蜜桃| 美女网站免费黄| 国产91视频| 四虎少妇做爰免费视频网站四| 开心激情网站| 91精品久久久久久粉嫩| 国产精品久久久久久久久久大尺度| 麻豆视频免费网站| 精品女同一区二区三区| 天天日天天摸| 国内精品久久久久| 91精品国产一级毛片国语版| 国产又粗又黄又爽又硬| 久久成人精品| 一起草无码在线| 国产午夜精品一区二区三| 亚洲电影在线| 99大香蕉| av资源网址| 欧美综合一区| 夜夜福利| 狠狠躁18三区二区一区| 九九精品久久| 人人妻人人艹| 国产高清无码视频| 无码免费一区二区三区电影| 久久精品三区| 秘书| 国产黄色一级大片| 超碰不卡| 成人午夜毛片| av亚洲欧洲日产国码无码苍井空 | 9l视频自拍蝌蚪9l视频成人| 人人偷人人摸| 国产极品jizzhd欧美| 国产精品性爱视频| 亚洲av色图| 成人免费黄色| 国产精品久久777777毛茸茸| 黄片视频大全免费看| 娇妻被朋友在客厅呻吟动漫| 99久久婷婷国产一区二区三区| 成人网战| 国产家庭乱伦| 精产国品第一页| 久久波多野结衣| 精品99久久久久成人网站免费 | 久久精品一区二区| 少妇超碰| 国产精品对白久久久久粗| 欧美日韩在线视频| 国产精品vⅰdeoXXXX国产| 日本精品久久| 影音先锋成人资源AV在线观看| 99re这里只有| 激情综合网欧美| 亲子乱V一区二区三区免费看| 亚洲精品国产suv一区| 精品成人| 国产自慰网站| 久久精品人妻少妇一区二区| 欧美精品区| 伊人网综合| 亚洲av网站| 国产精品一区在线播放| 特级无码| 亚洲无码在线视频观看| 亚洲第一成人网站| 欧美簧片| 国产精品久久久久久吹潮| 操逼视频无码| 精品www| 人人人操| 精品少妇| 精品综合久久久| 91久久精品| 久久综合国产| 18禁黑丝| 亚洲午夜久久久水多多影视| 99视频这里有精品| 欧美少妇性爱| 91精品国产色综合久久不卡粉嫩| 激情丁香花五月天按摩| 亚洲国产日韩三级av探花| 一区一区操逼的网| 国产精品操逼视频| 日韩人妻在线视频| 亚洲电影在线观看| 亚洲精品第一页| 亚洲国产精品无码一线岛国| 欧美性爱综合| 无码不卡视频| 亚洲综合图片| 久久精品人妻一区二区| 蜜乳中文无码H| 国产精品国产三级国产普通话三级| 精品人伦一区二区色婷婷 | 在线无码视频| 精品久久国产| 一级毛片av| 精品日韩欧美| 中文字幕无码精品亚洲35 | 色天堂在线| 日本免费久久| 欧韩精品视频免费观看| 欧美日韩国产二区| 亚洲精品伊人| 久久亚洲一区| 黄网站免费看| 日本aaaa| 日韩一区二区视频| 91久久精品国产91久久公交车| 尤物视频网站在线观看| 日韩在线视频一区| 一级毛片久久久久久久女人18| 国产精品久久久久久白浆| 中文在线а天堂中文在线新版| 日本免费久久| 91精品91久久久久77777| 国产一区黄色| 国产在线观看一区二区| 深喉| 中文字幕人妻系列| 久久久久国产| 国产东北女人做受av| 91免费在线视频| 少妇交换HD中文| 中文字幕人妻无码| 日韩三级免费观看| 91高清视频| 欧美日韩一区二区三区四区| 丁香五月社区| 婷婷国产| 日韩区欧美区| 中文字幕一区二区无码| 国产日韩一区| 欧美福利一区二区| 91偷拍视频| 黄色国产在线| 日本精品一区二区| 精品无码Av| 婷婷综合久久一区二区三区男男| 超碰这里只有精品| 国产激情| 高潮毛片无遮挡免费高清无码| 国产日韩欧美高潮无码一区二区| 国产无套白浆一区二区三区| 日韩 精品 无码 系列 另类| 国产精品久久AV无码| 色网在线播放| 热久久免费视频| 国产一二三内射在线看片 | 一本无码视频| 欧美熟妇精品一区二区蜜桃视频 | 十区操逼| 亚洲欧洲无码AAA片在线观看| 免费色色网站| 亚洲永久精品免费| 欧美第二页| 久久性爱俺| 成人三级片在线观看| 思思网站| 99久久婷婷国产精品综合| 黄频在线播放| 成人精品视频| 天天躁日日躁狠狠躁av无码老牛| 中文字幕在线观看免费视频| 日本免费一级片| 久久午夜夜伦鲁鲁一区二区| 日本黄色一级视频| 日本三级视频| 波多野结衣无码一区| 亚色在线| 人人妻人人艹| 少妇精品无码一区二区免费视频| 亚洲综合社区| 国产婷婷| 国产福利视频导航| 亚洲免费在线视频| 一区二区三区中文字幕在线观看| 中文字幕一区二区三区乱码| 孕妇孕交视频| 国产女同| 亚洲三级图片| 97中文字幕在线观看| 中文字幕在线免费| 国产精品欧美久久久久一区二区| 91一区二区| 九九精品久久| 国产成人精品久久二区二区| 国产吃奶A片一区二区| 黄色国产在线| 黄色一级网站| 一级片在线视频| 黄色片网站在线观看| 亚洲喷水无码一区丰满爆乳少妇| 中文字幕人妻无码系列第三区| 国产91丝袜在线播放| 国产流白浆| 国产一区二区视频免费观看| 亚洲国产成人va在线观看天堂| 黄色免费AV| 人妻毛片A一级毛片免费看| a毛片免费看| 国产污视频在线| 欧美日韩精品一区二区| 亚洲天堂无码一区| 激情久久五月天| 福利视频一区| 成人大片在线观看| 欧洲一区二区在线观看| 成人片黄网站色大片免费毛片| 国产精品无码一区二区三级不卡不 | 口爆吞精视频| 色七影院| 天天色天天日| 日韩毛片在线观看| 日韩精品一区二区三区免费视频| 色无码视频| 中字幕人妻一区二区三区| 九九热国产| 亚洲AV无码一区毛片AV| 久久精品91| av亚欧| 亚洲欧洲精品一区二区| 国产A片| 日韩无码电影| 国产熟女乱伦| 国产午夜小视频| av亚洲欧洲日产国码无码苍井空| 操逼视频国产| 日韩欧美一区二区三区四区五区 | 乱子轮熟睡1区| 黄色福利片| 一区二区三区亚洲无码| 91国内揄拍国内精品对白| 日本人妻丰满熟妇久久久久久| 十区操逼| 国产一级毛片国语一级A片厂百度| 日韩精品视频在线| 国产精品亚洲欧美在线播放| 黄色三级片网址| 久久大香蕉| 91AV色| 亚洲在线视频| 探花三区| 一级香蕉,黄色片| 影音先锋国产资源| 综合无码| 日韩黄色网络| 国产一区高清|