综合欧美一区二区三区,免费?Ⅴ中文字幕无码久久,人妻精品动漫H无码网站,岛国精品无码在线观看,亚洲一区二区日韩,欧美一区二区放荡人妇,无码人妻精品一区二区三区66,中文视频无码一区二区三区视频

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
亚洲中文字幕乱码无码一区二区 | 国产无套内射又大又猛又粗又爽| 天天日夜夜骑| 日韩夜夜高潮夜夜爽无码| 亚州Av无码| 国产精品久久久久久久久久久久久四虎| 狠狠做六月爱婷婷综合aⅴ| 久久久天堂| 成人性爱视频免费在线观看| 男人的天堂在线视频| 秋霞久久| aa一级特黄大片| 裸体久久女人亚洲精品| 国产在线拍偷自揄拍精品| 4388国产成人无码| av第一福利导航| 日韩三级片免费看| 久色91| 日日干日日操| 国产精品国产三级国产普通话三级| 成人在线观看网站| 四虎久久久| 综合天天色| 91麻豆精品91久久久久同性| 久久久久久国产精品免费播放| 成人午夜sm精品久久久久久久| 欧美另类视频| 福利导航第一品| AV无码专区| 天堂在线一区| 日本操逼逼| 欧美日韩一区二区在线| 在线观看高清无码| 暗交老女一区二区三区| 日韩精品免费一区二区三区竹菊| 91精品久久久久久久久| 欧美亚洲三级| 噜噜噜噜人人澡夜夜天堂| 亚洲AV片无码久久五月| 91成人国产| 东北浓毛老妇国语对白| 欧美日韩精品久久久免费观看| 在线观看一级黄片| 伊人精品久久| 91久久久精品国产一区二区爱豆 | 黄色成人在线| 五月丁香中文字幕| a黄色澳门免费观看| 亚洲激情一区| 亚洲精品无码一区二区三区网雨| 国产一区二区三区精品视频| www.精品视频| 日韩av电影在线观看| 欧美日韩一区在线| 日本黄色三级片| 萍萍的性荡生活第二部| 丰满少妇被猛烈高清播放| 日日朝屄| 丁香五月天导航| 一区二区三区av| youjizz国产| A一级黄色片| a毛片免费看| 欧美精品无码少妇a 6 2v久| 玖玖精品| 蜜桃久久av无码牛牛影视| 日韩欧美性爱| a在线视频| 日韩无码视屏| 日本一区二区在线| 五月天婷婷综合| 国产69Av| 一级毛片久久久| 日本人妻中文字幕| 日本操逼网| 国产69Av| 丰满人妻妇伦又伦精品国产| 黄网站无限看免费无码| 日韩 精品 无码 系列 视频| 亚洲一区二区在线视频| 高清无码网站| 91福利影院| 人人人操| 2014av天堂网| 国产高清无码视频| 自拍偷拍第一页| 伊人超碰| 人妻精品| 免费看黄色一级片| 高清无码一区| 国产一级毛片无码AAAAAA看| 亚洲乱伦图片| 无码精品久久| 91AV亚洲| 国产无码高清视频在线观看| 特黄A片| 欧美精品少妇| 九九久久国产精品| 国产真实精品久久二三区| 日韩精品网| 久久免费影院| 男女无套 在线观看网站| 精品导航| 91成人无码看片在线观看| 黑人AV一区| 欧美强奸乱论| 欧美日日干| 日本三级韩国三级美三级91| 国产精品一区二区三| 亚洲一级毛片| 成人黄色一级片| 欧洲一区二区三区| 欧美一级特黄A片免费看视频小说| 日韩黄色电影网站| 国产一级自拍| 秋霞午夜一区二区三区视频| 亚洲综合成人激情另类小说| 成人性爱视频免费观看| 久久久天堂国产精品女人| 国产中文自拍| 国产综合自拍| 久久久毛片| 成人欧美日韩| 亚洲无码视频免费在线观看| 日韩欧美三级视频| 亚洲午夜久久久水多多影视 | 国产精品偷伦视频免费观看国产| 黄频在线播放| 中文字幕综合网| 国产精品无码一区二区三区 | 超碰在线人人草| 亚洲AV怡红院| 婷婷国产| 国产做a视频| 无码视少妇视频一区二区三区| 免费黄网站| 懂色中文一区二区在线播放| 日本高清视频在线观看| 在线播放一区| 91老肥熟视频| 亚洲天堂网站| 亚洲制服丝袜在线观看| 日韩黄片免费在线观看| 一级毛片免费播放视频| 黄色在线网站| 日韩一区二| 91亚洲国产成人久久精品网站| 国产一区二区无码视频| 国产精品一级片| 真人一级毛片| 好色婷婷| 一级丰满老熟女毛片免费观看 | 琪琪午夜成人理论福利片| 国产超碰人人模人人爽人人添| 一夜强开两女花苞| 自拍偷拍精品| 国产综合精品| 国产精品操逼视频| www.超碰在线| 琪琪人妻一区| 国产熟女高潮一区二区三区| 波多野结衣一区| 日韩一区欧美| 午夜精品一区| 免费在线成人网| 丁香五月天婷婷| 国产一区视频在线播放| 免费人妻精品一区二区三区| xxxx18一20岁hd| 欧美一级性爱| 国产a区| 欧美黄片儿| 欧美日批视频| 久久久久一区二区精码AV少妇| 99国产精品| 91精品人妻| a视频在线| 国产成人精品久久| 97精品视频| 午夜私人天堂| 日本日逼视频| 国产麻豆乱伦| 日本一区二区在线| 欧美美女一区二区三区| 麻豆射区| 人妻少妇| 丁香五月天天| 经典三级在线观看| 日本东京热视频| 极品人妻videosss人妻| 久久久久亚洲AV成人无码电影| 无码在线免费| 人妻性爱视频| 丁香婷婷在线| 丁香九月婷婷| 久草精品在线| 国产激情无码| 高清免费av| 玖草在线| 欧美精品一区在线| 久久久久久久久久国产| 国产欧美日韩在线观看| 99re国产| 免费国产91| 婷婷久久综合| 精品无码国产一区二区三区高跟 | 黄色大片网址| 中文字幕网址在线| 国产女人18毛片水真多1KT∧| 男人天堂色| 成人一区视频| 国产亚洲无码在线| 性免费视频| 免费看日本伦人伦A片| 欧美日韩无码精品| 高清无码啪啪| 精品一区二区三区在线观看| 久久久久国产一级毛片| 日韩欧美二区| 欧美α片在线播放| 国产一级AV黄片| igao激情| 免费无码国产在线观看观喷水| 色欲影视综合网| 中文字幕第九页| 亚洲成人久久久久| 又长又粗又爽美女高潮视频| 亚洲最新网站| 日韩欧美少妇| 亚洲欧美日韩久久| 中文字幕亚洲中文精品乱码在线 | 午夜一级毛片| 国产黄色网| 男人天堂2024| 欧美日韩中文字幕| 亚洲精品一区杨思敏| 人人摸人人干人人色| 女人18片毛片90分钟免费| 午夜黄色影院| 国产精品伦一区二区三级视频| 亚洲高清无码在线观看| 真人一级毛片| av小网站| 亚洲熟女性爱| 高清无码小电影| 四虎影院国产精品| 操逼欧亚| 国产嫩草一区二区三区在线观看| 日本一区二区在线| 日韩三级在线观看视频| 国产精品99久久久久久白浆小说| 在线观看视频一区二区三区| 大香蕉大香蕉一级黄色片| 久久理论片| 亚洲综合色图| 日木精品人妻| 黄片无码| 亚洲色欲www| 人妻少妇精品视频一区二区三区| 久久久久国产精品视频| 91亚色在线观看| 精品九九九| 国产精品第5页| 婷婷超碰| 大地资源中文在线观看官网免费| 欧美视频亚洲视频| 肥臀熟妇真爽一区二区| 69av视频| 色91精品久久久久久久久| 无码国产精品一区二区色情八戒 | 人人操人人操人人操毛片| 秋霞手机在线观看| 欧美人伦| 午夜有码| 天天干天天干天天| 自拍视频国产| 无码精品一区二区三区潘金莲| 亚洲精品系列| 一级特黄aaaaaa大片| 日韩黄色一级片| 精品一级A片一区二区免费视频| 一级特黄视频| 91超碰在线| 日日夜夜视频| 午夜在线观看免费视频| 免费操逼网站| 日韩欧美精品| 亚洲性网| 国产在线小电影| 91精品在线播放| 日本无码A片免费网站| 国产成人无码综合亚洲AV| 亚洲国产精品久久久久| 欧洲另类一二三四区| 四虎无码| 日本护士高潮大叫| 午夜无码影院| 26uuu精品国产| 国产在线视频第一页| 午夜美女福利视频| 国产亚洲精品久久久久久牛牛| 中文字幕一区二区无码| av色综合| 欧美一区二区三区成人片在线| 青青草国产| 久草免费福利视频| 亚洲AV激情无码专区在线播放| 婷婷无码视频| 91精品国产综合久久久久久丝袜| 国产精品久久久久久白浆| 操碰视频| 欧美午夜精品| 天天草视频| 久久精品国产亚洲av丁香| 三级少妇| 91AV视频在线观看| 99免费观看视频| 中国女人毛片一级A片| 中文字幕人妻熟女在线| 女同亚洲熟女女同| 午夜想操你逼| 啪啪东京热| 欧美日韩国产乱伦| 黄色动漫网站| 天堂网在线视频| 中文在线а天堂中文在线新版| 精品人妻一区二区三区久久夜夜嗨| 日韩无码人妻| 中文制服丝袜熟女AV亚洲| caoprom人人| 2018av天堂| 三级片在线观看网站| 高清无码免费| 爽灬爽灬爽灬毛及A片| 成人超碰| 日韩欧美精品在线| 被十几个男人扒开腿猛戳| 人妇视频一区二区| 日韩无码视频一区二区| 久久天堂| 日逼视频xxxxxXxXX| 日韩国产免费| 日韩久久人妻| 亚洲精品无码AAA在线播放| 日韩精品在线看| 日本人妻中文字幕| 欧美日韩偷拍视频| 韩国久久| 免费激情网站| 怡红院成人网| 久久99精品久久久久久琪琪| 99精品自拍| 久久久一| 国产伦精品一级二级三级妓女| 无码国产精品| 福利一区二区视频| 欧美午夜精品久久久久免费视 | 小俊┅┅快┅┅用力啊| 最近中文字幕在线MV视频在线| 中文字幕成人AV| 日韩美女福利视频| 午夜情深深| 欧美黑人又粗又大又爽免费| 久久精品视频一区二区| AV鲁丝一区鲁丝二区鲁丝三区| 亚洲综合视频在线| 天天干在线观看| 欧美国产精品一区| 国产精品va无码一区二区臀| 免费无码一区二区三区| 国产三级探花日韩| 亚洲无码偷拍| 国产精品资源| 日韩欧美综合| 激情操逼视频| 91偷拍一区二区三区精品| 国产乱码精品一区二区三区忘忧草| 黄片免费在线播放| 色婷婷av久久久久久久| 四季AV一区二区凹凸精品| 国产无码高清视频| 欧美多毛熟妇| 性生交大片免费看A| 秋霞乱伦| 欧美拍拍| 91久久精品国产91久久公交车| 日韩精品视频在线免费观看| 亚洲熟人妇一区二区三区| 黄色片免费观看| 中文字幕免费在线观看| 高清无码视频在线播放| 蜜乳av牢记| 国产精品系列视频| 久久99久久久无码国产精品按摩| 日韩美女一区二区三区| 超碰不卡| 久久99精品久久久久久国产越南 | 国产视频久久| 国内精品视频在线观看| 国产黄色影院| 91精品久久久久久综合五月天| 激情一区二区| 亚洲色偷精品一区二区三区| 国产喷白浆一区二区三区| av高清在线| 国产一级毛片精品A片在线美传媒| 国产吃奶A片一区二区| 婷婷开心激情网| 日本三级韩国三级美三级91| 视频福利在线| 自拍偷拍第一页| 欧美一级黄色片| 久久久久亚洲AV无码专区首护士 | 成人毛片网| 无码一级毛片一区二区视频孕妇| 丁香五月中文字幕| 99re热精品视频国产免费| 久久久久久九九九九| 午夜无码片在线观看影院| 久久久大香蕉| 日韩视频精品| 伊人网视频| 日韩欧美中文字幕在线观看| AV手机天堂网| 美女18禁网站| 日本精品成人无码中文字幕网址| 搞黄无遮挡| 粉嫩AV无码一区二区三区软件| 久久久久久av| 欧美激情五月天| 99操逼视频| 制服丝袜综合| 国产黄色一级大片| 性爱热免费视频| 欧美亚洲性爱| 91免费看视频| 亚洲成人自拍| 久久性精品| 美女视频一区二区三区| 91精品一区| 亚洲无码免费在线视频| 欧美三级黄片| 九九精品久久| 国产成人毛片| 天天射天天爽| 国产伦精品一区二区三区免费迷| 中文字幕一级| 成人精品视频在线| 乱伦五月天| 无码不卡免费中文字幕视频| 无码视频一区二区| 无码人妻精品一区二区二秋霞影院 | 国产精品99精品久久免费| 极品白丝 国产| 美女航空一级毛片在线播放| www.国产精品视频| 一区二区三区日韩欧美| 高清无码国产视频| 无码视频一区二区| 九九精品视频在线观看| 婷婷色九月| 欧美日韩在线视频播放| 一级高跟鞋精品毛黄片| 欧美日韩精品| 色呦呦在线观看视频| 国产一级视频| 9l视频自拍蝌蚪9l视频成人| 天天搞天天搞| 日本福利一区二区三区| 国产一级a毛一级a| 国产三级精品在线| 亲嘴视频| 日韩无码一级片| 一级肉体AAAA片免费看| 国产一级片网站| 天天鲁一鲁摸一摸爽一爽| 日韩成人无码| 伊人免费视频| 亚洲AV片无码久久五月| 99re热精品视频| 亚洲V国产v欧美v久久久久久| 国产一区中文字幕| 偷拍二区| 97国产视频| 乱精品一区字幕二区| 3P 内射 在线| 在线免费观看αV| 久热综合| 黄色av网站在线观看| 亚洲精品在线视频| 日本在线一区二区三区| 一级在线视频| 久精品在线| 无码人妻中文字幕| 亚洲精品久久久久久中文传媒| 天天操狠狠操| 国产精品亚洲天堂| 精品导航| 无码在线观看一区| 丰满饥渴老女人hd| 亚洲欧洲一区| 强奸乱伦大香蕉网| 日本AA大片在线播放免费看| 91精品无码少妇久久久久久网站 | 久久久久亚洲AV片无码| 人妻精品| 亚洲一区二区免费| 国产精品女| 久久国产欧美| 国产精品无码在线播放| 精品欧美一区二区精品久久久| 自拍三级片| 天天干天天谢| 国产精品呻吟| 精品久久电影| freepeople性欧美| 国产精品偷伦视频免费观看的| 国产伦精品一区二区三区免.费 | 少妇3p| 青青草国产| 国产精品18| 国产老熟女伦老熟妇精品| 亚洲欧美日韩精品久久亚洲区| 日韩AV免费在线| 中文无码日本一级A片久久影视| 五月婷婷av| 国产欧美日韩精品专区黑人| 中文字幕日韩精品无码内射| 人妻超碰导航| 又粗又大又爽| 乳色无码| 久久官网| 97国产色呦呦呦夜嗨嗨| 亚洲无圣光| 爱爱综合| 日本丰满熟女视频中文字幕 | 久久精品国产亚洲A| 尤物视频色| 在线观看视频无码| 四虎成人影院| 国产二级片| 凸凹激情在线视频观看| 国产91精品一区二区| 最新中文字幕在线| 日本性爱视频在线观看| 丁香五月黄| 精品人妻一区二区三区日产乱码卜 | 亚洲毛片一区二区三区| 亚洲三级片在线观看| 国产精品毛片无码一区二区| 91成版人在线观看入口| 国产操比一区| 午夜高清无码| 苍井空久久| Chinese老女人老熟妇HD| 日韩超碰| 国产最新网站| 久精品视频| 欧美一区二区在线免费观看| 91福利网| 国产精品久久久久久久AV超碰| 精品人妻无码| 国产毛片网站| 91大神精品| 日韩欧美在线免费| 国产精品久久久一区二区| 91精品人妻一区二区三区蜜桃| 国产淫图AV| 成人激情视频在线观看| 99精品99| 日本免费在线| 人人操人人爱人人干| A级黄片免费看| 日韩在线免费视频| 国产一区二区三区在线视频| 日本少妇三级片| 国产高清无码一区| 久久久久国产熟女精品| 97超碰免费在线观看| 日本免费久久| 美女黄色免费| 久久久黄色网| 中文字幕在线无码| AV综合| 天天操狠狠操| 91视频国产精品| 国产精品久久久久久久久免费高清| 亚洲国产精品无码一线岛国| 精品视频网站| 91丨九色丨勾搭| 精品人伦一区二区三电影| 国产高清精品软件| 国产黄色精品| 91一区| 日韩黄色大片| 五月天丁香| 日本福利一区二区三区| 亚洲AV综合色区无码| 一级a一级a爰片免免免下载| 全肉变态重口调教高辣小说| 高清无码成人片| 黄片av免费观看| 偷拍亚洲一区| 国产精品三级久久久久久电影| 波多野结衣一区| 99精品在线观看| 午夜精品福利在线观看| 手机在线看片AV| 国产精品久久777777毛茸茸| 亚洲精品自拍| 国产精品啪啪啪| AV怡红院| 国产一区精品| 国产裸体永久免费视频网站| 乱色精品无码一区二区国产盗| 亚洲精品区一区二区三区四区五区高| 无码操逼视频在线观看| 中文字幕免费在线播放| 婷婷五月天综合| 亚洲一区二区自拍| 丁香五月天激情| 乱伦熟妇| 色一代影院| 国产日韩欧美一区二区东京热| 日韩三级视频| 啪啪啪精品| 日本精品一区| 内射一区二区三区| 在线免费观看黄片| 午夜一级黄色片| 免费A片久久久久久16色| 在线不卡av| 日韩欧美一区二区三区在线观看| av日韩一区| 日韩在线中文字幕| 欧美黑人少妇高潮喷水| 伊人网综合| AV免费在线观| 国产小视频在线| 免费观看全黄做爰的视频| 亚洲天堂网站| 日本三级不卡| 无码精品久久| 亚洲国产网站| 免费无码国产在线电影| 欧美老熟妇一区二区三区| 亚洲一区自拍| 狠狠操97操| 欧美综合在线观看| 成人性生交大片免费看4| 欧美中出| 国产人人操| 国产精品久久毛片AV大全日韩| 国产一级理论片| 亚洲无码久久| 牲欲强的熟妇农村老妇女视频| 成人二区| 特黄特色60分钟免费| 日韩精品久久久久久久| 久久永久视频| 日韩无码专区| 亚洲精品不卡| 婷婷五月丁香五月| 国产一级片av| 躁躁躁日日躁网站| 中文字幕人妻系列| 国产精品178页| 亚洲无码精品| 日韩成人无码| 久久久久久人妻| 国产一区二区AV| 亚洲欧美日韩在线播放| 欧美一区日韩一区| 免费看成年人视频| 中文字幕操逼| 蜜臀导航| 国产精品久久久久久久一区探花| 黄网站免费在线观看| 91九色国产TS另类人妖| 久久综合av| 欧美日韩视频在线播放| 成人精品在线观看| 99久久精品国产波多野结衣图片| 日操夜操| 精品亚洲国产成人AV制服丝袜| 中文字幕无码精品| 亚洲免费天堂| 东京热不卡视频| 特级黄色一级片| 国产精品一区二区三区AV| 国产伦精品一区二区三区妓女下载| 亚洲精品乱码| 99er热精品视频| 一级片在线免费观看| 亚洲91| 一区二区三区四区| 2023年中文字幕无码不卡| 日韩一二三四五区| 天天激情| 成人三级片网站| 午夜精品国产| 成人做爰高潮片免费观看视频| 欧美成人一区二免费视频苍井空| 91在线视频免费| 久久精品视频免费| 国产精品18| 午夜视频入口| 亚洲av免费在线| 久久精品国产亚洲A| 色偷偷网站视频| 国产精品一区视频| 少妇精品| 视频在线一区二区| 亚洲熟女一区二区| 国产女人18毛片水真多1KT∧| 国产男人天堂| 久久久天堂| 日本黄色三级片在线观看| 色婷婷精品国产一区二区三区| 国产极品jizzhd欧美| av网站观看| 日本三级视频| 久久精品一区二区三区免费播放| 精品国产乱码久久久久久婷婷| 性虎精品一区二区三区| 91导航中文字幕| 一级黄色网| 色中只有这里有精品| 久久久精品人妻| 粉嫩av久久一区二区三区小说| 伊人久久久久久久久久久久 | 最新国产AV| 成人性爱视频免费在线观看| 国内精品久久久| 亚洲免费网址| 国内视频自拍| 琪琪无码午夜精品久久久久| 久久艹艹艹| 天天操天天干天天日| 色一情一乱一伦| 色婷婷五月天| 亚洲黄色一区| 特级黄色一级片| 久久瑟瑟| 久久无码电影| 四虎无码| 亚洲熟妇乱伦| 日韩三级片视频在线观看| 亚洲 欧美 激情 小说 另类| 精品无码人妻一区二区三区 | 大香蕉国产| 久久国产视频网站| 亚洲有码一区| 久久久久亚洲AV无码网站| 无码国产孕妇一区二区免费AV| 亚洲日本精品| 亚洲一区在线播放| 日本欧美在线观看| 亚洲精品自拍| 2014av天堂网| 国产精品自拍一区| 亚洲AV动漫| 国产美女毛片| 欧美一区视频| 亚洲人午夜射精精品日韩| 老司机福利在线视频| 躁躁躁日日躁网站| 一级久久| 天堂中文在线视频| 哦┅┅快┅┅用力啊熟妇在线视频| 欧美性爱一区二区社区| 极品白丝 国产| 欧美一区在线看| 牛牛影视精品国产伦| AV中文在线播放| 无码流出在线播放| 欧美一区二区三区免费细高跟视频 | 91人人操人人摸| 亚州人妻| 久久无码人妻丰满熟妇区毛片| 国产午夜精品一区二区三区嫩草| 亚洲三级片在线播放| 欧美一区二区三区视频在线观看| 日韩精品久久久久久免费| 一级性爱毛片| 久久91欧美特黄A片| 小黄片在线免费观看| 人人插人人操| 亚洲免费一区| 亚洲一区中文字幕| 风间由美一区二区| 麻豆精品蜜桃视频网站| 伊人网视频| 国产激情久久| 亚洲永久精品免费| 91久久九色| 国产精品一区二区在线观看| 日韩人妻一区| 国产精品自拍一区| 啪啪免费在线视频| 国产精品久久777777毛茸茸| 偷拍一区二区三区| 欧美熟妇另类久久久久久牛牛影视 | AV手机天堂网| 久久国产精品无码一级毛片| 婷婷国产精品| 91日韩| 秋霞午夜福利视频| 亚洲精品区| 国产精品久久久久桃色TV| a一级性爱啊视频在线免费看| 亚洲伦理一区二区| 69久久| 久久大香蕉| 丰满熟女人妻一区二区三| 国产一级A片无码免费下载樱花| 高h小月被几个老头调教| 性无码一区二区三区| 国产三级片在线看| 日韩不卡一区| 狠狠干av| 日本精品无码aⅴ片视频| 中文字幕第九页| 久久精品久久精品| 国精品无码一区二区三区在线| 成人在线观看网站| 日韩久久久久久久| 国产又粗又黄视频| 亚洲欧美视频在线观看| 欧美抽插视频| 新久久久久久一级毛片免费看| 国精品91人妻无码一区二区三区| 毛片99| 一级a免一级a做免费| 秋霞在线视频| 激情内射人妻1区2区3区| 亚洲欧洲精品一区二区| 91口爆吞精国产对白| 日韩精品无码电影 | 欧美一区二区在线观看视频| 国产成人精品区一二三影院竹菊 | 亚洲xx网| 色一情一乱一乱一区91Av| 无套内谢波多野结衣| 成av人片一区二区三区久久| 这里只有精品视频在线| 日韩国产成人| 亚洲三级片网站| 91口爆吞精国产对白| 一级av在线| 国产激情久久| 狠狠操av| 干少妇视频| 毛片免费播放| 国产精品亚洲一区二区无码| 韩日无码视频| 毛色毛片免费看| 少妇3P性爱自拍| 超碰在线欧美| 欧美精品二街| 特级全黄久久久久久久久| 99精品国产91久久久久久无码| Xx性欧美肥妇精品久久久久久| 一区二区三区高清| 九九在线免费视频| 一级二级三级黄片| 五月天婷婷丁香花| 九九久久亚洲| 18禁无码毛片精品久久久久久| 无码高清电影| 色婷婷五月天| 亚洲精品无码一区二区三天美| 日韩三级免费观看| 91美女视频在线观看| 国产女人18毛片水真多| 日本美女一区二区三区| 精品人妻少妇一级毛片免费| 国产精品国产三级国产aⅴ入口| 无码人妻一区二区三区免水牛视频 | 精品视频二区| 久去色| 国产免费一级片| 东京热男人的天堂| 国产精品一二三区| 国产一区二区三区三州| 欧美久久免费| 天天干夜夜爽| 四季AV无码专区AV| 国产AV国产精品无套内谢下载| 亚洲熟妇乱伦| 日韩视频第一页| 日韩成人无码| 色色视频网站| 亚洲视频中文字幕| 91色色色| 亚洲国产毛片| 明星A片无码一区二区| 日本中文字幕在线播放| 国产黄片免费观看| 午夜日韩无码| 久久精品国产精品| AV一级片| 亚洲无遮挡| 午夜国产视频| 福利120无码| 熟女导航| 制服丝袜在线播放| 高清无码成人网站| 成人在线免费视频| 超碰 97一区二区| 欧美三级在线播放| 国产精品av久久久| 99精品久久毛片A片| 又粗又爽又猛高潮的在线视频| 亚洲激情一区二区| 99亚洲精品| 国产91精品一区二区绿帽| 国产精品久久久久久免费播放| AV鲁丝一区鲁丝二区鲁丝三区| 一区二区三区亚洲| 亚洲精品黄色| 超碰成人福利| 国产手机在线视频| 91天天操| 大地资源网在线观看免费官网| 看片网址国产福利av中文字幕| 久久国产亚洲精品| 亚洲熟女一区二区三区|