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
色综合五月| 五月婷婷六月激情| 婷婷精品在线| 婷婷丁香熟女| 在线观看免费狠狠色丁香香综合| 日日干综合| 情趣视频66| 激情综合婷婷| 色色综合热| www.久久爱| 99r这里| 婷婷99热| 五月天激情日色在线| 国产欧美日韩性爱| 99热精品在线在线| 欧美情色电影一区二区| 99婷婷| 亚洲性图一区二区| 五月丁香偷拍| 182TV亚洲| 丁香五月天五码婷婷| 久久99综合网| 午夜微拍福利| site:xmssd.com| 色色射| 人与禽A片啪啪| 亚州婷婷五月激情综合| 影音先锋人妻出差| 欧美日本韩国亚洲| 久久三级视频| 五月丁香大相交| 操97在线观看| 丁香花五月天| 日日夜夜婷婷| 国产99久久久| 色婷婷在线播放| 九九色插| 99热99热99热99热| 亚洲激情综合| 色五月开心五月激情五月| 亚洲精品成人区在线观看| 五月丁香六月片| 日韩无码专区| 97在线/亚洲| 人妻在线观看视频| 色色色色色爱| 亚洲成人影视在线| 欧美激情丁香五月天久久婷婷一区| 婷婷月综合| 综久久久| 天天天综合网| 偷拍99在线视频观看| 色情丁香五月天| 9久久精品| 丁香五月天天| 99色色| 五月天啪啪网| 天天天摸夜夜夜玩| 九九成人电影婷婷| 深爱激情av| 精品影院| 亚洲综合网激情小说| 超碰chaompinm| 激情宗合 激情宗合| A久网| 成 人 片 免费播放| 久久婷婷五月综合97色一本| se99视频| site:hcxsz888.com| 五月婷婷中文字幕| 噜噜噜狠狠色综| 热五月婷婷| 婷婷久久在线| 色色色五月婷| 九色91国产| 成全二人免费| 丁香五月婷婷欧美激情-中文天堂最新版在线观看 | 亚洲精品字幕在线观看| 久婷婷五月激情| 激情五月丁香五月| 色天天狠狠干| 97啪在线观看视频| 99精品福利视频| 91美女啪啪| 激情五月色播五月| 激情综合网色五月| 丁香五月性| 天天性视频| 99热6这里只有精品| 人妻人人操| 婷婷伊人久久| 久久久色婷婷五月天| 婷婷丁香五月综合| 五月天婷a| 五月天婷婷丁香成人网| 丁香六月婷婷| 久热九九| 精品人妻伦九区久久AAA片麻豆| 这里只有精品热| 无码AV免费精品一区二区三区| www.狠狠操.co m| 777精品久无码人妻蜜桃| 色色色在线免费视频| 欧美日韩成人在线| 激情五月丁香婷婷| 操人91| 五月天综合网| av免费人人| 一起操 91N.com| 天天天天干| 久99久精品| 色色99| 婷婷六月爽| 开心五月天激情网| 最新va在线播放| 五月天激情久久| 激情五月天丁香| 五月色丁香综合| 成 人 片 免费播放| 五月色无码| 5月丁香综合图区| 久久久这里有精品| 色色色综合网| 欧美A片在线视频免费观看| 狠狠干在线| 黄色一级影片| 欧美成人精品三区综合A片| 亚洲aV写真天天综合网久久| 大香蕉婷婷五月| 午夜69成人做爰视频| 亭亭五月激情亚洲在线| 翔田千里 50岁 无码| 色五月激情五月| 欧美婷婷五月丁香| 亚洲婷婷在线播放十月| 99色在线观看视频者| 超碰三级片| 丁香五月天色| 日韩有码一区| 桃色成人网| 欧美五月丁香| 五月婷婷视频啪啪美女| 91无码高清| 成人无码髙潮喷水A片| 丁香五月婷婷色| www.henhenl| 国产黄色在线播放| 97综合在线| 日韩AV色色色| 亚洲欧洲另类| Caop在线| 亚洲精品久久国产片麻豆| 九九热99热| 成人短视频在线观看| 99久久精品免费精品国产_国产精品久久久久久_国产在线|日韩_久久国产精品电影 | 日本欧美成人片AAAA| 色五月婷婷开心| 五月天之色情综合网| 五月天激情小说网| 再深点灬舒服灬太大了添A片小说| 亚洲 精品 综合 精品| 超碰97色| 丁香五月激情网| 91狠狠综合网| 六月婷婷开心| 亚洲男人的天堂婷婷色五月| 操久久网| 91精产一区三区免费观看| 乱码视频午夜在线观看| 天天日夜夜拍| 色久影院| 99热97| 翔田千里无码| 伊人久久激情图区五月| 国产综合视频婷婷| 岛国资源站| 69精品国产久热在线观看| 999激情视频| 淫荡工a| 性韩日色婷婷五月天激情啪啪XXX| 超级碰 久久9| 日韩丁香涩| 五月激情天| 日韩欧美成人片| 狠狠狠夜夜夜| SS丁香五月婷婷| 大香蕉AV在线| 天天操婷婷| 香蕉色色网| 亚洲视频在线观看| 天天日天天操心| 香蕉婷婷色五月| 中文乱子伦视频| 五月婷婷开心色伊人| 久久久99久久| 亚洲、欧美、国产另类笫二区| 9999热在线| 国产 亚洲 中文在线 字幕| 五月婷婷片| 五月丁香久人妻中文| 可以直接看的av| 激情五月婷婷中文字幕| 色高清无码视频| 色婷婷成人丁香| 九色婷婷| 九九色婷婷| 狠狠爱婷婷爱| 大香蕉九操| 丝袜激情网| 狠狠久久婷婷| 91在线操| 超级黄色片| A级毛片高清免费不卡播放谢谢谢谢| 午夜激情四射影院| 丁香五月综合狠狠| 久综合4| 9色在线| 大学生高潮无套内谢视频| 亚洲中文字幕网| 91九色丨国产丨爆乳| 狠狠色噜噜狠狠| 99热这里只有精品99| 婷婷伊人网| 成人五月天视频播放| 成人电影丁香六月天| 亚洲激情区| site:publishdd.com| 天天综合亚洲综合| 丁香婷婷婷五月| 亚洲婷婷丁香五月视频| 亚洲丁香五月| 夜夜骑夜夜操| 26UUU欧美激情一区二区| 成人免费高清在线播放| 五月丁香综合啪啪| 久久综合久色欧美综合狠狠| 丁香五月成人| 丁香六月欧美| 91AV婷婷| 国产色色网址网站| 天天干天天操天天上| 婷婷六月天亚州| 特级片神马电影| 丁香五月婷婷骚视屏| 五月天色婷婷伊人网| 色五月丁香网| 五月天丁香综合久久国产| caop在线| 国产欧美熟妇另类久久久| 影音先锋男人女人| 色月丁| 丁香六月综合| 另类激情五月| 色五月aV| 97热这里只有精品| 99热在线观看精品| 国产精产国品一二三在观看| 久久R激情| 91嫩草国产线观看亚洲一区二区| 超碰99热精品| 丁香六月婷婷缴情欧美| 五月天另类小说久久小说网| 午夜爱爱网站| 少妇性BBB搡BBB爽爽爽视頻 | A A色色| 精品乱码久久久久| 亚洲操b| 国产亚洲精品久久久久秋霞不卡| 人人操91色| 婷婷五月综合久久中文字幕| 91热er| www.99情趣网| 色噜噜狠狠一区二区三区| 久久天堂网| 九九综合图片网| 99久久99九九99九九九| 伊人五月天婷婷| 婷婷五月在线观看| 人妻AV中文系列| 日韩有码一区| 人人玩人人橾| 五月天狠狠| 岛国av网站| 香蕉曰比| 99re在线观看| 色五月在线播放| 欧美成人A片AAA片在线播放| 婷婷五月丁香99| 99视频久久| 天堂婷婷丁香六月网| 亚洲精品一区无码A片| 婷婷五月天成人| 五月婷婷婷色| 六月丁香啪| 婷婷丁香五月激情| 中字幕久久久人妻熟女天美传媒 | 婷色五月天| 26UUU在线观看| 91人操| 另类在线| 九九碰九九爱97| 久久色五月天综合网| 日本九九九九| 黄色91在线观看| 婷婷五月天国产手机在线视频观看| 五月婷婷综合在线视频小说| 高清无码中文字幕影片| 色色五月天激情| 这里精品| 亚洲欧美在线观看| 九九色影视| 色五月综合激情| av亚洲国产小电影| 五月丁香色| 99免费热视频在线| www.色多多婷| 丁香久久AV| www.婷婷六月天| 久久9情免费| 婷婷五月激情六月丁香| 91九色精品女同系列| 丁香婷婷浪潮AV久久综合| 思思久久网| 四川BBB搡BBB搡多| 一起草日本| 99精品无码| 国产婷婷五月中文字幕高清| 视频色色色色色色| 丁香六月综合激情| 婷婷97狠狠干| 97色干| 成片免费观看大全| 新激情五月天色播| 嫩草AV久久伊人妇女超级A| 99超级碰碰| 婷婷精品性视频| 婷婷在线播放| 三区激情四射av| 天堂久久精品| 视频一二区| 色爱亚洲| 色婷婷色综合| 激情深爱五月| 影音先锋男人AV资源站| 丁香五月亚洲婷婷| 一区二区国产精品精华液| 亚洲无码成人网| 婷婷伊人久久| 久久久www| 亚洲A片成人无码久久精品青桔| 91在线资源| 青草热视频这里只有精品| 国产AV一区二区三区日韩| 日韩九九视频| 天天操婷婷| 五月婷婷色情| 六月丁香啪啪| 俺也去在线久久精品23欧美综合视频网站,丰满人妻一区二区三区在线视频53,丰满 | 成人版视频在线观看| 性爱综合网| 97涩涩丁香五月天| 亚洲视频在线网站| 69色色视频| 婷婷大香焦| 激情五月天色婷婷综合| 国产二区自拍| www久热com| 狠狠爱成人综合网| 播四月婷婷六月丁香| 亚洲第一成人AV| 婷婷五月丁香伊人| 免费在线a| 综合网激情| 狠狠色狠狠爱| 99热在线观看精品| 日本三级黄色大片| 成人做爰A片免费看视频| 亚洲 综合中文| 成人片黄网站色大片免费毛片| 激情综合五月激情17| 五月丁香啪| 美女婷婷六月色| 亚洲激情在线| 婷婷丁香五月亚洲| 丁香婷婷色情| 日韩无码色色| 五月丁香综合伦理片| 超碰人妻公开在线| 国产成人亚洲综合A∨婷婷| 婷婷九色| 久久五月天婷婷| 婷婷五月天777| 大香蕉久久| 国产精品热搜丁香五月婷婷| pom538精品视频| 天天看A片| 狠狠干天天内射| 婷婷五月天综合网| 激情五月丁香亭亭| 激情性爱五月天| 精品一二三区久久AAA片| 激情五月亚洲综合网| 先锋影音男人的天堂AV| 久久3p| 丁香五月天婷婷在线视频| 欧美性生交XXXXX无码小说| 婷婷综合伊人丁香| 五月丁香自拍| 亚洲字幕AV一区二区三区四区 | 婷婷综合网伊人| 婷婷五月天丁香久久| 七七九色| 婷婷综合五月激情| 成人AV综合在线| 成人丁香五月| 欧美婷婷五月天综合| 99伊人婷婷在线| 婷婷五月成人| 99色爱| 久久久性爱网| 五月丁香久久| 情色婷婷五月天| 久色成人| 综合色色综合| 日韩欧美三区| 色135综合网| 婷婷色五月综合丁香| 9色天堂| 香蕉综合在线| 日韩人妻操逼视频| 狠狠丁香| 婷婷五月另类网站| 色情五月综合婷婷| 婷婷综合网站| 久久五月婷| 色99最新网址| 五月丁香偷拍| 五月天久久综合婷婷| 七七九九色色| 丁香六月婷婷综合啪啪| 丁香五月天啪啪| 五月 成人 婷婷| 久久婷婷五月丁香| 91人人操.COM| www久久久久久久| www色五月| 99爱视频免费| 亚洲综合五月天综合| 91九色中文字幕女在线观看| 99在线视频网址在线观看| 色色婷| 色情五月婷婷| 五月丁香激情综合欧美| 亚洲啪啪啪啪| 伊人影院久久网| 日本爆乳片手机在线播放| 天天肏天天肏天天肏| 五月丁香啪啪综合网| 性生活视频98791| 玖玖99婷婷| 欧美激情综合色综合啪啪五月| 婷婷淫淫狠狠六月| 热99这里只有精品视频| 五月欧美色播| 久久女人天堂| 99伊人婷婷在线| 国产亚洲精品久久久久苍井松| 婷婷九月亚洲| 婷婷综合激情| 五月综合六月婷婷| 高清激情av在线观看| 91精品久久久久久久| 一起操 91N.com| 午夜天堂一区人妻| 人人摸人人摸| 中文字幕av在线| 五月丁香婷婷基地| 五月丁香六月婷婷视频| 色综合五月婷婷狠狠干| 六月色日韩| 在线视频你懂得| av无码电影| 91九色 熟| 成人在线观看精品| 欧美AAAA片免费播放观看| 色色网站在线| 欧美日韩999| 91丁香五月| 色播播婷婷| 五月天五月婷五月激情网| 日本婷婷丁香五月| 无码网| se99高清无码| 丁香五月精品视频| 天天日天天舔天天摸| 99久在线观看| 99热国内精品| 超碰99久久| 99久在线精品99re5热视频| 人人操操97| 五月色婷婷夜色| 思思干精品| 91色综合网站在线| 超碰69天堂| 嫩草免费视频| 亚洲成人日韩无码精品| 九 九九九AV| 欧美日韩一区二区三区四区| 中文字幕成人版| 沈娜娜av| 婷婷中文字暮| 久久宗合影| 99爱爱网| 日本在线99| 欧美日韩成人一区二区| www,久久久| 啪啪啪综合网| 4399在线观看免费高清电视剧| 天天综合激情| 欧美韩日AAA网站| 五月天色婷婷小说| 操一操插一插| 婷婷播5月| 欧美成人va| 五月丁香六月| 免费婷婷| www.五月天婷婷| 国产精品久久久爽爽爽麻豆色哟哟 | 天天舔天天| 日日操,夜夜爽| 人妻内射视频| 中文字幕在线免费| 99国产精品久久久久久久久久久| 久久天堂网| 99在线视频网址在线观看| 成人一区在线观看| caop在线| 亚洲色情一区二区三区四区| 日本五月视频| 暗卫含着她的乳尖H御书屋| 久久婷婷五月综合色奶水99啪| 色婷婷久久| 亚洲一区在线播放| 美女久久婷婷| 99丁香五月婷婷在线| 婷婷五月香蕉| 狠狠干五月丁香| 五月天欧美激情| 五月天精品| 欧美99| 婷婷在线播放| 日本A片一区| 久99视频在线观看| 天天操九九插| 婷婷五月天天爽| 97五月天| 日日操夜夜操中国无码| 成人在线精品| 色狠狠色狠狠| 丁香五月婷婷视频| 免费亚洲婷婷| 激情综合文学| www色色com| 国产在线6| 丁香婷婷基地| 成人综合网站| 99ri国产精品| 久久婷婷婷| 狠狠狠激情网| 天天肏高清在线| 国产精产国品一二三在观看| 国产色99| 亚洲激情99| 亚洲国产精品VA在线看黑人| 图片区 小说区 区 亚洲五月| 婷婷六月成人| 五月婷婷激情综合在线| 色色网站| 欧美成人AAA片一区国产精品| 一级无码作爱片| 色五月丁香五月五月婷婷| www.com五月天| 99色在线| 91精品人妻少妇无码影院| 久久996re热这里只有精品无码| 亚洲五月天激情| 激情又色又爽又黄的A片| 涩涩婷婷五月| 亚洲久久婷婷| 99热精品无码| 五月婷婷国产| 婷婷激情欧美| 九热视频| 五月婷婷色啪| 99热精国产这里只有精品| 久久五月网| 五月日韩中文字幕| 超碰人人艹| 99re8这里只有精品99re8热视频| 欧美肉大捧一进一出免费视频| 婷婷五月综合啪| www.五月天。com| 伊人五月综合网| 色五月,婷婷大香蕉| 婷婷色色播五月天| 婷婷天天日婷婷| 九月丁香八月婷婷久久综合久97| 婷婷五月天99| 丁香五月激情五月| 99资源在线视频| 深爱激情六月| 电影888午夜理论不卡| caop在线视频| 五月天婷婷乱论小说| 99在线观看| 亚洲视频a| 99爽视频| 影音先锋综合网| 亚洲激情综合五月婷婷啪啪| 性按摩玩人妻HD中文字幕| 综合性爱网| 丁香五月婷婷香| 久久久全国免费视频| 五月激情日本在线| 五月丁香| 天天综合色丁香| 中文字幕 中文字幕明步| 色婷婷成人色网| 另类的婷婷| 国产免费一区二区在线A片视频| 99啊精典免费视频| 婷婷五月天综合在线| 九九婷婷激情综合网| 日日婷婷不卡| 麻豆WWWCOM内射软件| 婷婷激情在线| 五月婷激情影院| 99热999| 婷婷激情社区| 免费日本aⅴ中文字幕 | 人人舔人人| 在线综合亚洲欧美65| 天天橾日日橾夜夜橾17| 日日夜夜国产| 激情WWW| 五月天开心激情综合网| 香蕉久久国产av一区二区| 另类激情五月在线视频欧美| 丁香五月婷婷亚洲激情四射| 狠狠做婷婷| 免费播放AV| 成人五月天视频| 色婷婷色和| 五月激情综合美女久久| 久久丁香婷婷色情综合| 九九热视频精品| 91久久日日| 色婷婷五月天小说网| 九九色婷婷五月天| 五月色丁香激情| 操91综合网| 亚洲第一精品成人999久久精品| 久久九九思思| 深爱激清网| 久久九九一區| 国产又爽又大又黄A片| 九九大香视频| 九九碰九九爱97超| 久久久妻人人人| 另类少妇人与禽zOZZ0性伦| 玖玖在线资源视频| 午夜大香蕉| AV天堂婷婷五月天| 激情国产五月| 女BBBB槡BBBB槡BBBB| 色综合久久综合中文综合网| 五月开心激情| 色婷婷五月天天天天天天天天天| 69精品人人人人人人人人人| 五月天婷婷网站| 五月婷三级片| 五月天婷婷乱论小说| 久久久久网站| 思思久久99| 色五月xxx| 久久性爱视频| 色综合色综合色综合色综合| 超碰资源在线| 五月天婷婷久久视频| ji'qing'luan'ren'lun| 九久久婷婷| 天天插天天日| 人人操99| 日韩一本在线| 国产欧洲欧洲精品久久| www.五月婷婷久久.com| 国产做爰视频免费播放| 一级操逼内射在线视频| 98色花堂98t.R| 精品人妻久久久久久| 色五月视频无码播放| 97成人视频| 综合激情在线| 五月天激情在线视频| www99精品日韩| 国产免费天天看高清影视在线| 色五月综合网| 超碰成人黄色网| 五月花成人| 伊人干综合| 五月色综合| 婷婷五月色综合| 亭亭丁香久久五月| 91VIP在线观看| 久月婷婷| 五月天婷婷乱| 另类在线观看视频| 一区二区国产精品精华液| www.成人婷婷综合| 色吧五月| 骚逼视频一区2区| 伊人碰碰碰| 99久久国产宗和精品1上映| 色九月丁香婷婷蜜桃在线观看| 色狠狠综合网| 99只有这里是精品| 99爽视频| 超碰av在| 欧美精品999| 伊人五月天婷婷| 九月丁香婷婷综合| 色五月婷婷7777| 91超碰在线观看| 丁香五月婷婷骚视屏| www.9797国产| 无遮羞AV| 久久99操| 一级操逼内射在线视频| 性五月激情| 婷婷五月丁香激情图片| 成人做爰高潮A片免费视频| 丁香婷在线| 欧美日本VA| 老师把我爽高潮了免费A片| 国产精品久久久久久久久久久久| 思思久久精品| 99色丁香婷婷综合网| 99超级碰碰| 一级片麻豆| 婷婷五月激情图片| 逼里香不卡| 六月色色综合| 人妻激情综合| 曰本aaaaaa丈片| 99精品视频在线观看| 狼人婷婷久久| 五月婷婷开心综合| 天天综合情| 五月婷婷日| 色综合视频在线| 成人综合网站| 一月婷婷色色| 欧美va在线| 97涩涩丁香五月天| 国产精品大香蕉| 亚洲成人av在线播放| 美女婷婷激情亚洲| 丰满少妇乱A片无码| 日本操碰碰| 99爱在线免费视频| 亚洲婷婷五月天激情综合| 高清无码.com| 色婷| 日本久久人| 99re久热| 婷婷不干网| 97色婷婷| 亚洲av日韩无码| 五月激情影视| 五月深爱网| 伊人青草成人| 色婷婷基地| 亚洲AV中文在线| 伊人五月婷婷国产视频| 超碰免费在线| 香蕉久久国产AV一区二区 | 亚洲人人操| 国产日韩亚洲欧美在线观看| 久久九九99字幕| 亚洲美女婷婷五月天| 久久久中文| 婷婷射图| 欧美日本99| 影音先锋男士资源网一区| 久久精品视频91| 国产乱子轮XXX农村| 色噜噜婷婷| 久久久WWW| 五月丁香婷婷综合久久| 丁香五月激情五月| 思思久久99| 久久这里只有精品22| 婷婷狠狠五月综合| 五月天伊人日日噜影片AV| 婷五月丁香| 亚洲综合色网站| 五月丁香久久| 五月天婷婷色| 99玖玖免费视频| 99毛片| 色婷婷四色| 伊人玖玖综合| 99人人操人人操人人精| 九九九九九九综合| 婷婷趴趴| 亚洲精品444久久久久久| 丁香五月婷婷影院| 成人国产网站| 久久五月天 91| 99超级超级超级碰| 日韩三级高清无码| 五月丁香A片| 97超碰在线观看免费| 91精品国产色猫| 日本一级大片| 99视频九九热| 天堂A∨在线| 婷婷色色五月天| 侠女刀之记忆电影在线看免费| 久久这里只有精品07| 五月婷婷成人w| A A色色| 五月婷婷无码专区| 天天操夜夜橾| 五月婷婷激情综合| 激情五月婷婷视频一区二区三区| 毛多色婷婷| 伊人激情啪啪| 中国女人做爰A片| 久久六月综合| 午夜欧美艳情视频免费看| 人妻第九页| 天堂在线婷婷| 超碰91人人操| 最新日本A片| 九九草热在线观看| 可以看的av| 婷婷播播五月天| 婷婷丁香五月亚洲17cao| 色五月激情| 国外亚洲成AV人片在线观看| 九月婷婷在线视频| 超级碰碰碰碰视频| 色婷婷成人| 亚洲女婷婷五月基地综合久久久| 永久精品| 思思久久精品| 婷婷视频在线| 久久色五月| 五丁香激情综合| 婷婷激情小说| 色播五月婷婷| 先锋影音男人的天堂AV| 久久人人超| 五月婷婷中文网| 国产毛片精品一区二区色欲黄A片| 九九久久精品| 丁香六月婷婷操逼网| 91成人电影| 五月丁六月香av| 播播五月天| 少妇AB又爽又紧无码网站| 婷婷激情综合色五月久久91| 久久之人妻| 丁香六月无码播放| 人妻无码精品一区| 成人AV播放| 欧美色色色| 狠狠狠夜夜夜| 色色色热| 亚洲无码猫咪| 日日噜狠狠色综| 久久婷婷五月天蜜桃| 亚洲人妻av伦理| 99热大| 99惹| 丁香久久久| 激情综合五月| 激情丁香六月| 久久免费视频62| 丁香六月情| 91久久九九| 激情综合网五月天天| 五月天激情综合| 夜夜操狠狠操| 操操碰| 丁香五月天堂| 蜜桃网999| 久久网日本| 91色情播放| 538在线精品| 九九色大香蕉| 激情五月婷婷老师| 国产真实乱了老女人视频 | 天天干天天干天天干| 操操操97| 婷婷亚洲日本| 怡春院| 五月婷护士| 男人天堂亚洲综合| 婷婷成人综合| 综合九九久久| 丁香五月激情婷婷| 少妇性BBB搡BBB爽爽爽电影| 五月婷六月| 青青草激情网| 久久99久久99精品,久国产,久久精品免费,99久在线,久久久久国产精品免费网站,9 | 午夜成人在线免费视频| 玖久精品视频9| 五月丁香六月婷| 性视频久久| 激情六月色| 黄色网址五月婷婷| 色情丁香五月天| 五月婷婷激情网| 丁香婷婷五月天成人| 亚洲成片在线观看| 热99国产精品| 99 色色吧| 亚洲色碰| 99色在线观看视频| 婷婷五月天激情诱惑| 国产 亚洲 中文在线 字幕| 噜噜噜噜婷婷五月天| 在线天堂9| 婷婷久久18| 婷婷五月天激情丁香| 久久精彩视频| 狠狠五月丁香色婷| 亚洲欧洲中文日韩久久AV乱码| 粉嫩av蜜桃av蜜臀av| 国产AV精国产传媒| 激情五月婷婷她| 六月丁香色婷婷| 精品日本视频444| 天天日,夜夜爽| 2018国产大陆天天弄| 激情q青青草在线婷婷| 色婷婷狠狠| 99操逼视频| 99精品热视频| 成人电影在线免费试看| 狠狠色大香蕉| 九九视屏| 五月四房| 日本人人干| 欧美性爱中文字幕| 色五月婷婷自拍| 日日夜夜干| 九九十99视频| 久99在线视频| 国产熟女大叫受不了| 色色激情五月| 97香蕉久久超级碰碰高清版| 99九九在线精品热动漫| 999激情视频| 丁香五月综合婷婷| 色呦呦免费观看| 色婷婷影院| 五月丁香婷婷网网网网| 江苏少妇性BBB搡BBB爽爽爽| 五月色情婷婷开心五月天| 国产又色又爽又黄又免费| 99re久热只有精品6在线直播| 亚洲无AV在线中文字幕| 国产精品-91JQ就要激情网91JQ6.91JQ27.CASA:16888 | 九九久久精品| 日本性激情色播| 依人大香蕉在钱1| 色五月丁香五月天| 五月丁香人妻| 91丨九色丨熟女丰满| 婷婷五月天黄色| 六月婷婷五月天| 午夜理论片最新午夜理论剧| AV亚洲AV永久无码精品网 | www。五月,com| 99热免费在线| 91久久精品无码一区二区三区| 91久久久久久久久18| se99高清无码| 欧美AAAA片免费播放观看| 人人看人人草人人摸| 大香蕉太香蕉视频97| 五月婷婷激情| 色综合激情| 亚洲无AV在线中文字幕| 国产美女最新VA在线免费观看| 婷婷99视频全集高清| 五月婷婷综合丁香视频| 97人凄人人操人人爽| 中文成人在线| 日日狠夜夜狠| 色五月自偷自拍婷婷婷婷| 色色免费网站| 深情六月婷婷综合久久| 激情小说色五月| 5五月综合网亚洲| 婷婷久热| 亚洲精品乱码久久久久蜜桃 | 丁香久月| 亚洲精品网站色视频| 婷婷色色亚洲| 欧美成人精品A片免费一区99| 永久无码色| 激情性爱五月天网页| 怡春院| 热的五码久久精品| 桃色五月婷婷| 综合一区二区三区| Se.婷婷五月天| 亚洲av电影网站| 久久色天堂| 狠狠人妻久久久久久综合丁香| 久久五月天丁香花| 丁香五月电影| 五月婷导航| 婷婷99中文字幕| 亚洲精品大片| 丁香花网站| 色五月婷婷成人视频| 涩玖玖免费视频| 丁香六月五月天| 欧美VA在线观看| 天天色丁香| 狠狠综合| 九九热99熟女| 男人综合网| 97色在线观看视频| 亚洲欧美中文字幕高清在线| 性一交一乱一美A片69XX| 中文字幕丰满乱孑伦无码专区| 免费一区二区三区| 五月天婷婷视频| 婷婷丁香五月综合激情小说| 丁香色五月 97干| 国产av网| 99这里只有| 99精品视频网站| 激情综合五月激情17| 久久婷婷色综合老司机| 成人做爰高潮A片免费视频| 激情五月婷婷五月丁香五月开心五月| 99激情网| 亚洲激情五月| 国产精品99久久久久久久女警| 香蕉AV777XXX色综合一区| 色情五月天se| 99热免费精品| 国产97在线日韩亚洲女人被黑人巨大| 九九热超碰| www.五月丁香| 午夜青草资源| 天天噜日日噜综合无码| 国产特黄色精品一区二区三区精品无广告| 综合色久| 五月婷婷成人| 五月天色五月天| 色婷婷香蕉| 亚洲AV永久无码影院黑人 | 久久只有精品| 另类图片天天影视在线观看| www.日日夜夜.com| 激情婷婷人妻| 丁香六月婷婷开心| 日韩婷久| 丁香五月天啪啪| 99热这里只有精品22| 亚洲精品永久久久久久| 欧美成人网婷婷综合在线| 色色吧综合| 久久图色4| 婷婷视频在线碰| 国产又色又爽又黄又免费| 久久激情五月婷婷| 色欲一二三| A A色色| 色色色视频免费无码 | 五月婷婷大香蕉| 六月丁丁香| 生活片五区| 婷婷激情六月| 亚洲无码九九| 97涩涩丁香五月天| 国产精品久久久99视频| 日本色99| 99啪在线| 热99热久| 日本久久激情| 青青青视频在线| 夜夜爽天天爽| 久9热在线视频| 天堂资源8| 激情五月婷婷综合| 天天色,天天日,天天做| 婷婷香五月综合激情| 99热这里只有精品13|