成人精品毛片v?一区二区_野战小树林大屁股少妇_久久精品一区二区三区不卡_国产福利拍拍拍三级视频_久久99国产综合精品免费懂色_亚洲AV午夜福利无码精品一区_欧美成人奂费视频_国产成8x人网站视频_白丝在线观看国产AV

2024

2024

  • Record 361 of

    Title:Swin-CDSA: The Semantic Segmentation of Remote Sensing Images Based on Cascaded Depthwise Convolution and Spatial Attention Mechanism
    Author Full Names:Kang, Yuhan; Ji, Jian; Xu, Hekai; Yang, Yong; Chen, Peng; Zhao, Hui
    Source Title:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
    Language:English
    Document Type:Article
    Abstract:As an important task in remote sensing image processing, semantic segmentation of remote sensing images has broad application prospects in many fields such as disaster warning and rescue, environmental protection, and road planning. Research on semantic segmentation of remote sensing images based on deep learning has made some progress, but there are still problems such as poor perception of small object features, loss of detailed information in deep feature extraction, and imprecise segmentation contours of small objects. To this end, we propose a new remote sensing semantic segmentation model Swin-CDSA, which copes these problems to some extent by designing cascaded deep convolutional modules (CDCMs) and spatial attention mechanisms (SAMs). CDCM extracts multiscale features by using multilayer convolutions with different layers but parallel fixed small-sized kernels, while SAM supplements the model's understanding of local and global information through a dual attention mechanism. We conducted experiments on the Potsdam and LoveDA datasets and achieved good results.
    Addresses:[Kang, Yuhan; Ji, Jian; Xu, Hekai; Yang, Yong; Chen, Peng] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China; [Zhao, Hui] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
    Affiliations:Xidian University; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS
    Publication Year:2024
    Volume:21
    Article Number:3003405
    DOI Link:http://dx.doi.org/10.1109/LGRS.2024.3431638
    數(shù)據(jù)庫ID(收錄號):WOS:001283693700005
  • Record 362 of

    Title:Hybrid Fiber-Single Crystal Fiber Chirped-Pulse Amplification System Emitting More Than 1.5 GW Peak Power With Beam Quality Better Than 1.3
    Author Full Names:Li, Feng; Zhao, Wei; Li, Qianglong; Zhao, Hualong; Wang, Yishan; Yang, Yang; Wen, Wenlong; Cao, Xue
    Source Title:JOURNAL OF LIGHTWAVE TECHNOLOGY
    Language:English
    Document Type:Article
    Keywords Plus:FEMTOSECOND; AMPLIFIER; KW; LASERS
    Abstract:A hybrid chirped pulse amplification system composed by the monolithic fiber pre-amplifier and a two-stage single-pass single crystal fiber amplifier was demonstrated. A maximum power of 68 W at the repetition rate of 100 kHz was obtained. The laser pulses were amplified and then compressed using a 1600 line/mm grating pair compressor. A short pulse duration of 358 fs and a power of 54 W were obtained at 100 kHz, corresponding to a peak power of 1.508 GW, to the best of our knowledge, this is the highest peak power ever obtained from single crystal fiber at repetition rate above 100 kHz due to the consideration of the third order dispersion which was engraved in the stretcher and the tuning capacity of higher-order dispersion compensation of chirped fiber Bragg grating. Additionally, the beam quality better than 1.3 was obtained. This high peak power CPA system with excellent comprehensive parameters will find various applications in scientific research and industrial applications.
    Addresses:[Li, Feng; Zhao, Wei; Li, Qianglong; Zhao, Hualong; Wang, Yishan; Yang, Yang; Wen, Wenlong; Cao, Xue] Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; State Key Laboratory of Transient Optics & Photonics
    Publication Year:2024
    Volume:42
    Issue:1
    Start Page:381
    End Page:385
    DOI Link:http://dx.doi.org/10.1109/JLT.2023.3312399
    數(shù)據(jù)庫ID(收錄號):WOS:001129777400014
  • Record 363 of

    Title:Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images
    Author Full Names:Li, Xuemei; Wang, Xing; Ye, Huping; Qiu, Shi; Liao, Xiaohan
    Source Title:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
    Language:English
    Document Type:Article
    Keywords Plus:COASTLINE EXTRACTION; NETWORK
    Abstract:The demarcation between the sea and the land, commonly referred to as the coastline, is of paramount importance for the dynamic monitoring of its alterations. This monitoring is essential for the effective utilization of marine resources and the conservation of the ecological environment. Addressing the challenges posed by the extensive expanse of coastal lines, which can complicate their acquisition and processing, this study utilizes remote sensing imagery to introduce an algorithm for coastal line segmentation. The algorithm integrates multiple networks to enhance its effectiveness. Innovations encompass the development of an extraction algorithm for coastal lines that are as follows. First, utilize an attention-guided conditional generative adversarial network (AC-GAN) model, which redefines the task of image segmentation by framing it as a style transformation problem. Second, a strategy for coastal line segmentation utilizes Dense Swin Transformer Unet (DSTUnet) to construct a densely structured model. This approach integrates Transformer to prioritize focal regions, thereby enhancing image and semantic interpretation. Third, a transfer learning framework is proposed to integrate multiple features, leveraging the strengths of different networks to achieve accurate segmentation of coastal lines. The study introduced two datasets, and the experimental results confirm that parallel network configurations and asymmetric weighting are superior in achieving optimal results, with an area overlap measure (AOM) score of 85%, outperforming the Unet by 5%.
    Addresses:[Li, Xuemei] Chengdu Univ Technol, Sch Mech & Elect Engn, Chengdu 610059, Peoples R China; [Wang, Xing] Natl Inst Measurement & Testing Technol, Elect Res Inst, Chengdu 610021, Peoples R China; [Ye, Huping; Liao, Xiaohan] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Ye, Huping] Chinese Acad Sci, Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China; [Qiu, Shi] Xian Inst Opt & Precis Mech, Chinese Acad Sci, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China; [Liao, Xiaohan] Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China
    Affiliations:Chengdu University of Technology; National Institute of Measurement & Testing Technology; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences
    Publication Year:2024
    Volume:62
    Article Number:4208312
    DOI Link:http://dx.doi.org/10.1109/TGRS.2024.3435963
    數(shù)據(jù)庫ID(收錄號):WOS:001288457800005
  • Record 364 of

    Title:Biomedical Image Segmentation Using Denoising Diffusion Probabilistic Models: A Comprehensive Review and Analysis
    Author Full Names:Liu, Zengxin; Ma, Caiwen; She, Wenji; Xie, Meilin
    Source Title:APPLIED SCIENCES-BASEL
    Language:English
    Document Type:Review
    Keywords Plus:CONVOLUTIONAL NEURAL-NETWORKS; PREDICTION; ALGORITHM; ENTROPY; CANCER
    Abstract:Biomedical image segmentation plays a pivotal role in medical imaging, facilitating precise identification and delineation of anatomical structures and abnormalities. This review explores the application of the Denoising Diffusion Probabilistic Model (DDPM) in the realm of biomedical image segmentation. DDPM, a probabilistic generative model, has demonstrated promise in capturing complex data distributions and reducing noise in various domains. In this context, the review provides an in-depth examination of the present status, obstacles, and future prospects in the application of biomedical image segmentation techniques. It addresses challenges associated with the uncertainty and variability in imaging data analyzing commonalities based on probabilistic methods. The paper concludes with insights into the potential impact of DDPM on advancing medical imaging techniques and fostering reliable segmentation results in clinical applications. This comprehensive review aims to provide researchers, practitioners, and healthcare professionals with a nuanced understanding of the current state, challenges, and future prospects of utilizing DDPM in the context of biomedical image segmentation.
    Addresses:[Liu, Zengxin; Ma, Caiwen; She, Wenji; Xie, Meilin] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Liu, Zengxin] Univ Chinese Acad Sci, Sch Optoelect, Beijing 101408, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:14
    Issue:2
    Article Number:632
    DOI Link:http://dx.doi.org/10.3390/app14020632
    數(shù)據(jù)庫ID(收錄號):WOS:001149358200001
  • Record 365 of

    Title:Study on Stray Light Testing and Suppression Techniques for Large-Field of View Multispectral Space Optical Systems
    Author Full Names:Lu, Yi; Xu, Xiping; Zhang, Ning; Lv, Yaowen; Xu, Liang
    Source Title:IEEE ACCESS
    Language:English
    Document Type:Article
    Keywords Plus:WIDE-FIELD; ELIMINATION; DESIGN
    Abstract:To evaluate the ability of space optical systems to suppress off-axis stray light, this paper proposes a stray light testing method for large-field of view, multispectral spatial optical systems based on point source transmittance (PST). And a stray light testing platform was developed using a high-brightness simulated light source, large-aperture off-axis reflective collimator, high-precision positioning mechanism and a double column tank to evaluate the stray light PST index of spatial optical system. On the basis of theoretical analyses, a set of calibration lenses and stray light elimination structures such as hoods, baffle and stop are designed for the accuracy calibration of stray light testing systems. The theoretical PST values of the calibration lens at different off-axis angles are analyzed by Trace Pro software simulation and compared with the measured values to calibrate the accuracy of the system. The testing results show that the PST measurement range of the system reaches 10(-3)similar to 10(-10) when the off-axis angles of the calibration lens are in the range of +/- 5 degrees similar to +/- 60 degrees. The stray light test system has the advantages of wide working band, high automation and large dynamic range, and its test results can be used in the correction of lens hood and other applications.
    Addresses:[Lu, Yi; Xu, Xiping; Zhang, Ning; Lv, Yaowen] Changchun Univ Sci & Technol, Natl Demonstrat Ctr Expt Optoelect Engn Educ, Sch Optoelect Engn, Changchun 130022, Peoples R China; [Xu, Liang] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
    Affiliations:Changchun University of Science & Technology; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS
    Publication Year:2024
    Volume:12
    Start Page:33938
    End Page:33948
    DOI Link:http://dx.doi.org/10.1109/ACCESS.2024.3369471
    數(shù)據(jù)庫ID(收錄號):WOS:001178226700001
  • Record 366 of

    Title:Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
    Author Full Names:Qin, Xing; Gao, Xin; Yang, Xiaoxu; Xie, Meilin
    Source Title:PHOTONICS
    Language:English
    Document Type:Article
    Keywords Plus:AFFINE SYSTEMS; ALGORITHM; IMAGE; MAGNITUDE; L-2(R-D); RECOVERY
    Abstract:This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt-pepper noise.
    Addresses:[Qin, Xing; Yang, Xiaoxu; Xie, Meilin] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Qin, Xing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Gao, Xin] Beijing Inst Tracking & Telecommun Technol, Beijing 100094, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:11
    Issue:1
    Article Number:71
    DOI Link:http://dx.doi.org/10.3390/photonics11010071
    數(shù)據(jù)庫ID(收錄號):WOS:001151554300001
  • Record 367 of

    Title:Attention Network with Outdoor Illumination Variation Prior for Spectral Reconstruction from RGB Images
    Author Full Names:Song, Liyao; Li, Haiwei; Liu, Song; Chen, Junyu; Fan, Jiancun; Wang, Quan; Chanussot, Jocelyn
    Source Title:REMOTE SENSING
    Language:English
    Document Type:Article
    Keywords Plus:REFLECTANCE RECOVERY; COVER
    Abstract:Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods.
    Addresses:[Song, Liyao] Xian Technol Univ, Inst Artificial Intelligence & Data Sci, Xian 710021, Peoples R China; [Li, Haiwei; Chen, Junyu; Wang, Quan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Liu, Song] Nanchang Hangkong Univ, Sch Measuring & Opt Engn, Nanchang 330063, Peoples R China; [Fan, Jiancun] Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China; [Chanussot, Jocelyn] Univ Grenoble Alpes, Grenoble INP, GIPSA Lab, CNRS, F-38000 Grenoble, France
    Affiliations:Xi'an Technological University; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Nanchang Hangkong University; Xi'an Jiaotong University; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
    Publication Year:2024
    Volume:16
    Issue:1
    Article Number:180
    DOI Link:http://dx.doi.org/10.3390/rs16010180
    數(shù)據(jù)庫ID(收錄號):WOS:001141352200001
  • Record 368 of

    Title:Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS
    Author Full Names:Su, Yunhao; Han, Junfeng; Ma, Caiwen; Wu, Jianming; Wang, Xuan; Zhu, Qinghua; Shen, Jie
    Source Title:IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
    Language:English
    Document Type:Article
    Keywords Plus:PERFORMANCE; SENSOR; SIGNAL
    Abstract:Magnetohydrodynamic angular rate sensor (MHD ARS) can precisely detect angular vibration information with a bandwidth of up to one kilohertz. However, due to secondary flow and viscous force, it experiences performance degradation when measuring low-frequency angular vibrations. This article presents an adaptive Kalman filter that uses online angular random walk (ARW) estimation to correct for the low-frequency error of MHD ARS, where a microelectromechanical system (MEMS) gyroscope is used to measure low-frequency vibrations. The proposed algorithm determines the signal frequency based on the ARW coefficients and adjusts the measurement noise covariance to achieve accurate fusion results. Thus, the method solves the problem of frequency-dependent variation of the amplitude response of the sensors in data fusion. Initially, the algorithm calculates the ARW coefficient recursively utilizing the measurement signals of both sensors. Then, the operational frequencies of both sensors are determined by analyzing the correlation between the ARW coefficient and frequency. Subsequently, in the Sage-Husa adaptive Kalman filter (SHAKF), the Kalman gain matrix is adjusted by modifying the measurement noise variances of both sensor signals individually. Moreover, the stability of the proposed algorithm is achieved by introducing an adaptive matrix to constrain the measurement noise covariance estimation. In the experiment, the fusion effects of single-frequency and mixed-frequency signals are tested separately. The experimental results show that for frequency variation and frequency mixing, the proposed algorithm in this study significantly improves the fusion results.
    Addresses:[Su, Yunhao; Han, Junfeng; Ma, Caiwen; Wang, Xuan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Photoelect Tracking & Measurement Technol Lab, Xian 710119, Peoples R China; [Su, Yunhao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wu, Jianming; Zhu, Qinghua; Shen, Jie] China Aerosp Sci & Technol CASC, Shanghai Acad Spaceflight Technol, Shanghai 200240, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:73
    Article Number:9509510
    DOI Link:http://dx.doi.org/10.1109/TIM.2024.3375962
    數(shù)據(jù)庫ID(收錄號):WOS:001219576300010
  • Record 369 of

    Title:Intelligent Space Object Detection Driven by Data from Space Objects
    Author Full Names:Tang, Qiang; Li, Xiangwei; Xie, Meilin; Zhen, Jialiang
    Source Title:APPLIED SCIENCES-BASEL
    Language:English
    Document Type:Article
    Abstract:With the rapid development of space programs in various countries, the number of satellites in space is rising continuously, which makes the space environment increasingly complex. In this context, it is essential to improve space object identification technology. Herein, it is proposed to perform intelligent detection of space objects by means of deep learning. To be specific, 49 authentic 3D satellite models with 16 scenarios involved are applied to generate a dataset comprising 17,942 images, including over 500 actual satellite Palatino images. Then, the five components are labeled for each satellite. Additionally, a substantial amount of annotated data is collected through semi-automatic labeling, which reduces the labor cost significantly. Finally, a total of 39,000 labels are obtained. On this dataset, RepPoint is employed to replace the 3 x 3 convolution of the ElAN backbone in YOLOv7, which leads to YOLOv7-R. According to the experimental results, the accuracy reaches 0.983 at a maximum. Compared to other algorithms, the precision of the proposed method is at least 1.9% higher. This provides an effective solution to intelligent recognition for spatial target components.
    Addresses:[Tang, Qiang; Li, Xiangwei; Xie, Meilin; Zhen, Jialiang] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China; [Tang, Qiang; Xie, Meilin; Zhen, Jialiang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:14
    Issue:1
    Article Number:333
    DOI Link:http://dx.doi.org/10.3390/app14010333
    數(shù)據(jù)庫ID(收錄號):WOS:001139153100001
  • Record 370 of

    Title:Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-image-free phase retrieval from single-shot hologram
    Author Full Names:Tian, Xuan; Li, Runze; Peng, Tong; Xue, Yuge; Min, Junwei; Li, Xing; Bai, Chen; Yao, Baoli
    Source Title:OPTO-ELECTRONIC ADVANCES
    Language:English
    Document Type:Article
    Keywords Plus:RECONSTRUCTION; MICROSCOPY
    Abstract:Digital in-line holographic microscopy (DIHM) is a widely used interference technique for real-time reconstruction of living cells' morphological information with large space-bandwidth product and compact setup. However, the need for a larger pixel size of detector to improve imaging photosensitivity, field-of-view, and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution. Additionally, the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image. The deep learning (DL) approach has emerged as a powerful tool for phase retrieval in DIHM, effectively addressing these challenges. However, most DL-based strategies are data- driven or end-to-end net approaches, suffering from excessive data dependency and limited generalization ability. Herein, a novel multi-prior physics-enhanced neural network with pixel super-resolution (MPPN-PSR) for phase retrieval of DIHM is proposed. It encapsulates the physical model prior, sparsity prior and deep image prior in an untrained deep neural network. The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods. With the capabilities of pixel super-resolution, twin-image elimination and high-throughput jointly from a single-shot intensity measurement, the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
    Addresses:[Tian, Xuan; Li, Runze; Peng, Tong; Xue, Yuge; Min, Junwei; Li, Xing; Bai, Chen; Yao, Baoli] Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China; [Xue, Yuge; Bai, Chen; Yao, Baoli] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; State Key Laboratory of Transient Optics & Photonics; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
    Publication Year:2024
    Volume:7
    Issue:9
    Article Number:240060
    DOI Link:http://dx.doi.org/10.29026/oea.2024.240060
    數(shù)據(jù)庫ID(收錄號):WOS:001321134300003
  • Record 371 of

    Title:Multilevel Attention Unet Segmentation Algorithm for Lung Cancer Based on CT Images
    Author Full Names:Wang, Huan; Qiu, Shi; Zhang, Benyue; Xiao, Lixuan
    Source Title:CMC-COMPUTERS MATERIALS & CONTINUA
    Language:English
    Document Type:Article
    Keywords Plus:DIAGNOSIS ALGORITHM; PULMONARY NODULES
    Abstract:Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computeraided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows: 1) Concentration on the lung parenchyma region: Based on 16 -bit CT image capturing and the luminance characteristics of lung cancer, we proffer an intercept histogram algorithm. 2) Focus on the specific locus of lung malignancy: Utilizing the spatial interrelation of lung cancer, we propose a memory -based Unet architecture and incorporate skip connections. 3) Data Imbalance: In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples, we scrutinize the existing loss function and suggest a mixed loss function. Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy, measured as Area Overlap Measure (AOM) is superior to 0.81, which markedly ameliorates in comparison with conventional algorithms, thereby facilitating physicians in diagnosis.
    Addresses:[Wang, Huan; Qiu, Shi; Zhang, Benyue; Xiao, Lixuan] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Peoples R China; [Qiu, Shi] Fourth Mil Med Univ, Sch Biomed Engn, Xian, Peoples R China; [Xiao, Lixuan] Univ Illinois Urbana Champion, Champaign, IL USA
    Affiliations:Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Air Force Military Medical University
    Publication Year:2024
    Volume:78
    Issue:2
    Start Page:1569
    End Page:1589
    DOI Link:http://dx.doi.org/10.32604/cmc.2023.046821
    數(shù)據(jù)庫ID(收錄號):WOS:001199394600019
  • Record 372 of

    Title:Underwater Single-Photon Profiling Under Turbulence and High Attenuation Environment
    Author Full Names:Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Li, Xiangyu; Shi, Heng; Feng, Xubin; Su, Xiuqin
    Source Title:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
    Language:English
    Document Type:Article
    Keywords Plus:REGULARIZATION
    Abstract:Underwater single-photon imaging is challenging, as the transmitting path presents turbulence and strong backscattering noise; both facts degrade the image, thus hindering its applications in real world. However, current studies on underwater single-photon modeling have generally overlooked the potential impact of water turbulence on imaging performance. This oversight may result in an inaccurate characterization of the optical propagation process in realistic imaging environment. This letter proposed a joint denoising and deblurring method with regularization by denoising (JDD-RED) for underwater single-photon image that include the modeling of turbulence and the tailored restoration model, improving the performance by considering blurring mechanism, as well as advanced signal processing method. This method is validated on numerical experiments by employing joint deblurring and denoising tasks. Compared with the PICK-3-D algorithm, the JDD-RED reconstruction results demonstrate that more detailed information can be retained while denoising. In addition, the results show an average improvement of 1.48 dB in peak signal-to-noise ratio (PSNR) and 60% in structural similarity (SSIM), proving the superior performance of the JDD-RED algorithm.
    Addresses:[Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Li, Xiangyu; Shi, Heng; Feng, Xubin; Su, Xiuqin] Chinese Acad Sci, Key Lab Space Precis Measurement Technol, Xian 710119, Peoples R China; [Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Su, Xiuqin] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Shared Technol & Facil, Xian 710119, Peoples R China; [Wang, Jie; Su, Xiuqin] Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China; [Wang, Jie; Hao, Wei; Chen, Songmao; Xie, Meilin; Shi, Heng; Su, Xiuqin] Pilot Natl Lab Marine Sci & Technol Qingdao, Qingdao 266200, Peoples R China
    Affiliations:Chinese Academy of Sciences; Chinese Academy of Sciences; Xi'an Institute of Optics & Precision Mechanics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Laoshan Laboratory
    Publication Year:2024
    Volume:21
    Article Number:6501605
    DOI Link:http://dx.doi.org/10.1109/LGRS.2024.3432931
    數(shù)據(jù)庫ID(收錄號):WOS:001287339700008
国产69久久久欧美黑人A片| 天天日日综合| 五月丁香色六月激情干大屄| 大香蕉手机视频| 色五月97| 狠狠插狠狠| 色99网| 欧美片第1页 综合| 激情综合网五月在线播放| 五月婷色激情五月| 婷婷五月天色| 五月天色婷伊人| 亚洲人人操| 日韩成人精品中文字幕| 天天操夜夜夜夜爽| 九九伊人网| 激情婷婷视频在线| 新伍月婷婷| EEUSS鲁片一区二区三区| 婷婷综合一二三| 六月天六月婷| 大片国产片日本观看免费视频| 91九色无码日韩 | 五月天婷婷中文字幕在线播放| 九九色99| 久久狠狠干| 婷婷欧美激情| 97色精品视频| 久久成人天| 99热只有| 亚洲思思热久| 日韩成人网址| 色综合久久综合中文综合网| 99综合自拍| 97伦色婷婷| 99色丁香婷婷综合网| 五月激情六月综合| 久草五月| www.婷婷五月| 激情文学 综合 色| 婷婷俺去也| 欧美 日韩 成人 在线| 综合五月婷婷| 丁香五月综合首页| 成人五月网| 国产综合丁香五月天| 久久99成人性爱高清视频| 国产69精品久久久久999小说| 91色综合网| 六月丁香婷婷综合在线| 91久久精品视频| 玖玖资源站蜜臀| 极品美女久久久久久久久久久| 久久婷婷六月| 中字幕视频在线永久在线观看免费 | 99热最新| 99热免费在线| 五月色婷婷综合色| 亚洲免费视频在线| 天天日,夜夜爽| 丰满少妇猛烈A片免费看观看| 综合激情伊人影视在线| 粉嫩AV久久一区二区三区| 亚洲小视频免费看| 青青青国产精品免费观看| 夜夜骑操AV| 夜夜操狠狠操| 狠狠干2007| 亚洲亚洲人成综合网络| 综合色综合| 1024国产在线| 这里有精品2| 色吊丝99| 日本欧美成人片AAAA | 人妻在线网站| 婷婷永久在线| 亚洲丁香五月综合| 影音先锋 萱萱| 久久久久久五月天| 激情视频综合| AV操操操| 激情婷婷综合| 五月丁香六月激情| 五月天婷婷在线播放| 久久久久九九九九视屏小说88| 五月花婷婷最新| 欧美.亚洲.日韩.天堂| 五月天婷婷久色| AV中文网| 91九色精品| 久久98| AA片在线观看视频在线播放| 色射婷婷五月天| 69人人操人人爽| 婷婷五月激情中文字幕| 久久激情五月| 精品爱欲五| 另类小说激情五月天| 99热精品10| 色色色色色色色色五月先| 六月婷婷av| 九九在线视频| 亚洲第一精品成人999久久精品| 亚洲色综合色网| 亚洲无码成人| 亚洲一区先锋影音| 国产综合婷婷| 日本精品人妻无码77777| 99视频在线观看欧| 伊人狠狠丁香婷婷综合尤物| 在线观看视频一区| 亚洲色五月婷婷| 97狠狠色| 丁香,开心成人,久久| 狠狠狠狠狠干| 五月丁香天天| 久xxxx| 综合色五月天| 狠狠五月天激情| 99精品高潮| 狠狠精品干练久久久无码中文字幕 | 99视频激情四射| 婷婷亚洲天堂| 色yeye欧美| www狠狠| 色婷五月天亚洲| 另类图片五月天婷婷| 五月婷婷99热| 久久色情综合免费网站| 精品婷婷五| 99se丁香| 3p日韩网站视频| 色444综合网| 欧美丁香婷婷五月| 激情婷婷六月天| www.日本91| 欧美日韩AAAAA| www.91AV.com| 成人做爰黄A片免费看直播室男男 最近中文字幕大全免费版在线 | 色五月婷婷在线观看第一页舔| 综合五月丁香六月婷婷| 亚洲成人乱码av网站| A在线观看| 五月天精品| 色八月婷婷| 波多野结衣成人作品在线| 亚洲超碰在线| 99热在线观看免费精品| 日日干干天天干| 久久人操-久草婷婷-成人AV| 五月婷婷综合网| 亚洲综合色色| 天天综合精品| 五月婷婷之美女图片| 中文字幕婷婷五月天| 六月婷婷日| 99久久国产宗和精品1上映| pacopacomama 070722_670 素人奥様初撮りドキュメント 103 大久保純子 | 这里只有精品热| 91色在线 | 日韩| 91男同视频| 综合色色网| 五月天激情国产综合婷婷| 精品无码av丁香五月激情| 97干在线看| 影音先锋91在线资源站| 激情综合五月开心狠狠| 五月天激情社区| 色五月婷婷激情综合网| 欧美操人| 九九久久五月天| 丁香久久激情俄| 天天干一干| 欧美激情综合色综合啪啪五月| 天天舔天天摸| 狠狠色综合精品视频在线| 五月婷婷少妇之| 玖玖综合色| 婷婷久久伊人| 九九视屏| 久草五月天| 丁香六月婷婷高清| 99a级片| 久久天堂色| 性爱网五月婷婷| 欧美1页| 国产成人亚洲综合A∨婷婷| 丁香婷婷色色| 99噜噜| 色婷婷五月综合| 激情五月天伊人影院| 婷婷五月开心中文字幕在线| 婷婷色五月色妇| www婷婷亚洲| 人人操Av| 五月色欧美| 婷婷五月免费观看| 丁香五月天殴美激情| 亚洲 25P| 丁香五月天婷婷中文| 深爱网深爱综合网| 99热这里有精品24| 丁香五月天激情网| 26uuu精品国产| 亚洲色婷婷五月| 天天做 天天爱| 久久欧洲综合网| 天天射夜夜骑| 九九色综合九九色| 国产午夜精品一区二区| 色综合久久44| 亚洲精品一区中文字幕乱码| 国产午夜精品久久久久九九 | 婷婷免费视频| 精品久热| 亚洲AV成人在线观看| 婷久久高清| 日本91在线播放| 伊人大香蕉爱聚| 亚洲精品444久久久久久| 久色大香蕉| 成人短视频免费观看| 毛v一区二区视频| 在线看黄色| www.综合久久.com| 五月丁香亚洲校园欧美| 激情五月网站| 色五月天激情| 丁香在线视频| 九九视频这里只有精品| 中文字幕一区中文亚洲| 五月婷婷综合在线视频| 全高清无码视頻| 激情综合五月丁香| 六月色狠狠色| 99热.com| 久久激情五月天| 91日韩在线| 成人精品视频99在线观看免费| 亚洲欧美中文字幕高清在线| 大香伊人婷婷影院| 97九色| 99'无码| 人人人操97| 天天干电影| AV在线免费播放| 五月激情小说| 伦乱人妻| 精品久热| 99热这里只有的精品视| 久久人人九九| 丁香五月天视频| 大香蕉懂9| 五夜婷婷| 狠狠干五月丁香综合网| 日韩欧美一级大黄网站| 色青青电影色五月| 猫咪伊人久久| 丁香五月亚洲综合| 超碰九九热| 精品国产乱码久久久久久夜深人妻 | 五月婷婷六月天| av中文在线| 色色热| 97热九九| 五月婷人妻| 婷婷五月天AV| AA片在线观看视频在线播放| 殴美日韩成人| 亚洲五月天伊人| 激情网五月天| 日韩内射美女人妻一区二区三区 | 一区二区乱视频码| 五月天婷a在线| 色五月丁香五月天| 疯狂做受XXXX高潮A片| 亚洲图色五月天| 久久这里都是精品免费| 五月丁香六月婷婷激情视频在线观看免费| www.色婷婷| www网站在线观看| 亚洲一个色| 操一操干一干| 午夜福利视频合集1000| 综合久久影院| 逼里香不卡| 视频色色色色色色| 激情六月天| 日韩无码乱轮| 日日操天天爽| 欧美三级巜人妻互换| 99精品视频推荐| 色五月天综合| www激情| 超碰在线观看caop| 激情综合五月.....| 婷婷久久五月天丁香| 超碰只有精品在线| 五月丁香六月婷婷,婷| 色婷婷丁香五月| 激情五月天婷婷| 综合色99| 91婷婷搞| 色婷在线视频| 美女丁香五婷婷| 欧美一区二区在线观看| 在线五月婷婷小电影| 久久五月天综合| 91亚洲天堂| 国产综合色婷婷精品久久| 四色AVwww| 人妻丰满精品一区二区A片| 精品一二三区久久AAA片| 婷婷五月丁香色播| 久久性爱视频网站| 婷婷伊人综合| 丁香五月天激情网| 久热一区| 五月天婷婷色播在线网| 香蕉久久av一区二区三区| 亚洲在线操| 色婷婷激情| 操逼电影免费看| 久久婷婷五月综合激情国产| 香蕉AV777XXX色综合一区| 丁香婷婷五月激情| 久9久9久9久9久9久9| 无码九九九九| 99色色色色| 丁香综合日产精品久久| 国产成人va在线| 香蕉久久国产AV一区二区| 日本va欧美va欧美va精品| 91色色色18| 国产黄色一级片| 丁香色六月| 五月婷婷影| 爱狠射| 26uuu另类亚洲欧美日本一| 97超碰婷婷五月天| 丁香五月天激情小说| 亚洲精品中文字幕无码A片蜜桃 | 婷婷激情五月天桃花网| 六月丁香色婷婷| 亚洲色99综合天堂| 99爱视频在线观看这里只有精品| 日逼影音先锋AV男人资源站| 无码99| 狠狠色大香蕉| 天天肏在线观看| 婷婷六月啪啪| www.色五月| 深爱五月月天| 秋霞免费视频| 玖玖福利视频资源| 日本在线观看aaa 99| 激情综合色婷婷六月天| 99免费在线视频| 日韩黄色电影| www久视频com| 婷婷丁香色情五月天| 日韩在线视频9色| 激情五月天色婷婷综合| 91综合网| 人人操人人爰人人一天天碰夜夜拍夜夜爽-中国A级毛片天天看天天谢… | 亚洲激情五月天| 台湾综合丁香五月蜜桃| 久久女婷| 小视频久久久aaa| 激情久久网 | 成人网站av免费网站推荐| 九九性爱网| 五月久久婷婷| 精品一二三区久久AAA片 | 亚洲久久婷婷丁香五月天| 91se视频| 欧美成人va| 变态 另类 在线 | 丰满老熟妇BBBBB搡BBB| 婷婷五月天第四色| 操日本三片99| 亚洲第一成人无码A片| 夜夜夜夜做天天天做无码视频| 影音先锋美国A| 色五月激情婷婷| se99热久久一本| 亚洲综合五月天婷婷丁香| 99精品视频免费在线播放| 久色网| 热热久久99| 骚片AV蜜桃精品一区| 久久深爱激情网| 七月激情六月婷婷综合在线播放| www.婷婷五月| 久久婷婷一级片| 丁香激情五月少妇| 色婷婷在线视频| 午夜电影网VA内射| 亚洲旡码| av大片在线| 五月丁香在线| caobi四区| 伊人大香蕉毛片| 91久久九久久九久久九久久九久久| 五月色综合| 久久大大香| 婷婷激情区| 亚洲天堂有码| 69精品人人人人| 婷婷 月 丁香| 久久香蕉影院| 67194线路二在线观看| 亚洲综合无码| www.sezonghe| 免费九九热| 超碰九热| 九九免费视频| 色爱亚洲| 天天干天天日日| 1024成人在线观看| av大香蕉| 九热视频| 久热这里只有精品6| 婷婷五月蜜桃成人桃色丁香| 色婷亚洲五月丁香| 伊人激情| 99久热| 曰日爽日日操| 五月婷婷天天色| 超碰不卡在线| 五月婷婷香蕉| 一本大道嫩草AV无码专区| 裸体美女丁香五月天。| site:jszngf.com| 日韩黄色电影| av在线免费播放观看| 五月婷婷之综合激情| 这里只有精品2| 色在线免费观看| 六月婷婷综合| 91色噜噜狠狠狠狠色综合| 色婷婷九月综合| 色婷婷五月天激情| 97在线/亚洲| 亚洲图片 丁香婷婷| 熟妇人妻中文字幕无码老熟妇| 五月综合激情视频在线| 婷婷基地五月色| 成人看片网站| 色五月激情综合| 国产亚洲99久久精品熟| 精品无码日本蜜桃麻豆| 丁香久久| 婷婷刺激综合| 丁香五月天资源网| 色噜噜在线| 狠狠夜夜五月丁香| 欧美在线视频99| 激情综合色婷婷啪啪六月天| 色婷婷四虎| 久热无码| 亚洲精品一区二区午夜无码| 99热免费| 五月丁香狠狠爱| 无码激情AAAAA片-区区| 丁香婷婷深情五月亚洲| 26uuu欧美激情另类| 管管補管管紱| 热这里只有精| 婷婷色五月婷婷姐妹| 色色操| 五月天国产| 九九热av| 99热日韩| 亚洲在线操| 美国十月色婷婷在线观看| 中文字幕性爱丰满| 99热这里只有精品1998| 激情综合婷婷| 日韩六十路91性交电影| 天天日夜夜高潮| 9er热在线精品视频| 六月色色| 99re思思热在线视频| 五月天丁香综合| 1024成人在线观看| 一二区成人电影| 天色综合网| 狠狠狠狠青草| 色爱综合五月| 九九久久高清| 亚洲天堂无码| 丁香五月情| 丁香婷婷色| 日韩在线视频网站| 婷婷五月天堂| 狠狠色婷婷777| 日韩啪啪网| 久久久人妻不卡| 婷婷第六色| 伍月婷丁香婷| 97五月婷婷| 色吊丝永久访问网址| 亚洲激情综| 中文字幕在线不卡| 亚洲人成网亚洲欧洲无码久久| 婷婷色五月婷婷姐妹| 日韩黄黄| 日本va欧美va欧美| 五月婷婷开心网| 推油小说| 日日夜夜天天综合| 99久久精品费精品国产| 五月天丁香色色| 9|在线观看视频| 伊人大香蕉在线视频| 丁香婷婷五月色成人网站| 99ri在线| 丁香五月综合在线| 五月婷婷丁香狠狠撸久久| 久久婷婷成人综合色怡春院| 搡BBBB搡BBB搡| 亚洲综合成人网| 专区无日本视频高清8| 婷婷激情四射| 五月天激情图片网| 色五月色五天色情网| 丁香五月自拍| 亚洲成人va| 丁香婷婷色情| 国产五月天欧美色| 久9视频免费播放| 色播五月| 碰超亚洲| 色三级色三级| 99视频精品8| 人人视频人人干人人做| 婷婷丁香色五月| 五月丁久久| 五月亭大香蕉| 热久久77777| 色婷婷社区| 日本三级黄色大片| se色婷婷视频| 五月丁香激情四射综合| 99热这里只有精品13| 六月婷婷色| 亚洲无码99| 影音先锋五月天婷婷丁香在线观看| 色你久久| www.婷婷,com| 色婷婷免费视频| 久久91精品国产91| 婷婷五月天久久综合88| 婷婷久久免费看| 夜夜干 夜夜操| 色色a| 久久综合香蕉国产国产蜜臀AV| 大香蕉九操| 丁香五月av在线| 97人人做| 婷婷色五月色| 香蕉综合在线| 琪琪色网址| AV堂狠狠干| 丁香六月色婷婷| 五月综合色| 日本一级大片| 五月天成人在线精品| 99re在线播放| 99久久户外勾搭| 综合色情网| 噜噜久| 国产一区二区三区影院| 亚洲色综久久五月| 激情婷婷六月| www.狠狠| 欧美性猛交99久久久99| 久久这里只有国产视频| 五月丁香香蕉| www.综合久久.com| www.26uuu.com亚洲电影| 91一起操| 久99精品视频| 精品久久久久久久久久久久人妻| 99re8这里只有精品99re8热视频| 欧美啪啪网| 六月丁香婷婷在线波多| 五月花婷婷| 超碰97干| 五月激情六月综合| 字幕网AV中文字幕| 深爱 五月天| 久久婷丁香五月| 婷婷五月精品中文| 538任你爽视频不一样的| 99九九99九九九视频精品| 99视频在线精品免费观看2| 荡乳尤物3HP1V5| 久久大香蕉视频| 久久婷婷五月综合伊人| 国产精品美女久久久久AV超清| 欧洲一区二区| 激情深爱五月天| 久操香蕉| www,五月丁,com| 最近中文字幕2019视频1| 亚洲综合色色| 啪啪操超碰| 去色色五月天| 激情小说五月欧美亚洲丁香| 国产黄色在线播放| 熟女激情网| 久色网| 97热久久| 天天日天天舔| 亚洲免费视频网站| 激情五月婷婷开心网| 久久视9精| 久久网思思| 欧美美女一区二区三区| 怡红院99| 四LLL少妇BBBB槡BBBB| 亚洲综合激情五月久久| 99精品久久久久久久婷婷| 欧美性爱一区| 人人操人人爰人人一天天碰夜夜拍夜夜爽-中国A级毛片天天看天天谢… | 2017狠狠干| www.色五月| 伊人久久中文网| 99视频这里有精品| a片在线免费观看一区| 亚洲综合丁香五月天| 99热超碰人| 2025天天操| H亚洲| 久久99性爱| 亚洲色区17| 国产性爱亚洲是图| 色婷婷操逼网| 裸体做A爰片毛片A片免费| 99五月婷| 激情六月丁香| 亚洲久热| 香蕉AV福利精品导航| 婷婷五月天在线观看av| 亚洲日本欧美产综合在线| 九九草热在线观看| 五月天丁香色色| 国语精品探花| 人人草碰| 国产激情在线| www久| 天天做天天爱天天高潮| 丁香五月婷婷成人综合| 《亚洲操B久久免费在线观看,亚洲操B久久在线播放》在线播放 - 高清资源 - 97 | 99热.com| 在线视频99| 五月婷亚洲精品AV天堂| 天天操天天日天天爱| 亚洲亚洲人成综合网络| 丁香五月在线视频黑人| 日本97人人| 天堂中文在线资源| 天天色播| 国产精品久久久久久福利| 另类小说五月天激情| 激情婷婷五月社区| 天堂网啪啪| 国产毛片操B| 天天干天干| 久久久久网站| 这里只有精品视频免费在线观看| 久久视频这里都是精品| 婷婷碰碰| 激情丁香六月| 包操45分钟网站| 婷五月天影院| 久久99激情| 激情深爱综合| 亚州欧美黄色电影| 婷婷午夜综合| 蜜臀A∨在线水帘洞| 婷婷香蕉精品| 丁香五月色五月| 色五月综合婷婷久久综合婷婷久久综合婷婷久久综合婷婷久久 | 五月丁香啪综合| 美女丁香五月天| 超极99精品| 国产精品视频免费看| 狠狠色婷婷7| 男女av免费看| 九色婷婷| 丁香五月欧美色综合| 亚洲AV无码成人精品区电影网| 亚州操人在线视频| 五月丁香激情在线| 欧美日本黄色| 婷婷亚洲天堂| 在线天堂官网| 丁香伊人激情| 9久久网| 免费视频WWW在线观看网站| 亚洲欧美另类在线23p| 色热久| 婷婷D区| 狼友超碰| 午夜婷婷五月天在线| 91青娱乐青青草| 久久婷婷六月综合| 色99热| 天天 青草 丝袜制服 在线| 色吊丝永久访问网址| 国产 亚洲 中文在线 字幕| 开心婷婷五月| 精品综合久久久久久五月天| 91日韩美女被插视频| 欧美亚洲熟妇一区二区三区| 超碰国产AV| 五月激情偷拍婷婷| 站长推荐无码播放| 五月婷婷香蕉| 激情综合网五月激情| 五月婷精品| 99热 免费| 婷婷综合在线播放| 色色五月婷婷| 五月婷婷性爱| 婷婷丁香18| 天天激情视频| 人人97碰| 九月婷婷综合在线| 中文字幕不卡高清视频在线| 五月天婷婷免费| 色99热| 天天爽天天弄| 五月激情啪啪啪| 97在线天堂| 国产99久久久国产精品免费看| 辣椒视频| 五月丁香六月婷婷亚洲视频| 天天爽天天爽天天爽天天爽天天爽| 一操久久| 91丨九色丨国产| 色婷婷成人做爰A片免费看网站| 婷婷五月色综合| 久久久久久丁香五月| www久久五月com| 色你久久| 欧美碰碰| 黄网在线免费| 无码少妇高潮喷水A片免费| 色九亚洲| 色五月婷婷在线观看| 婷婷丁香五月六月激情| 五月丁欧美| 伊人激情综合网| 91久久免费| 涩丁香91| 日韩限制级大尺度黑料泄密大尺度视频一区二区在线观看 | 久久在线人妻| 婷婷五月天视| 91九色首页| 99狠狠| 久久精品五月| 日本在线观看aaa 99| 激情五月综合网| WWW.桔色成人.COM| 精品久热| 97AV在线视频| 天天肏高清在线| 四月丁香五月婷婷久久| 九九免费精品| 五月丁香婷中文| 激情综合网,五月| 婷婷五月色花丁香社区| 97自拍视频在线| 激情综合五月天| 伊人久久大香线蕉av一区| 久久婷婷资源| 久久综合婷| 久热久re| 久热re在线视频| 99久久色| 国产综合丁香五月天| 丁香婷婷婷婷十二月在线观看视频| 亚洲岛国电影| 亚洲黄色操逼| 天天日日| 色情激情五月婷婷| 色婷婷精| av在线不卡播放| 五月婷婷黄色毛片| 男人的天堂97| 午夜婷婷久久| 超碰色综合| 综合色色婷婷| 99热九九这里只有精品| 婷婷色婷婷亚洲成人| 久久九九99| 午夜色色色极品视频| 久久婷婷六月| 热久久99视频| 天天色,天天操,天天射| 涩涩婷婷五月| av人人操| 色色性爱视频| 久久久久婷| 91avse| 天天天天天日| 国产69久久久欧美黑人A片| 国产午夜精品一区二区三区嫩草| 风流少妇A片一区二区蜜桃| 国产综合视频在线观看一区| 亚洲日韩成人三级av| A片天天| 久艹伊| aaa久久| 日韩成人电影AV| 激情婷婷内射| 激情五月天色爱| 亚洲第一第二网站| 色九月婷婷丁香| 午夜五月天| 久久一级视频| 成人五月天。COM| 色五月婷婷自拍| 欧美内射AA| 久久婷婷五月草视频在线播放| 色色亚卅| 玖玖婷婷五月天| 99日本在线| AA片在线观看视频在线播放| 欧美情色一区| 亚洲视频无| 五月婷在线观看| 婷婷激情综合色五月久久,色婷婷丁香花,丁香婷婷五月情天,久久婷婷五月综合色 | 色无码| 国产黄色在线观看| 变态另类色图| 91美女被操| 五月丁香 久久久| 婷婷五月天激情基地| 午夜成人网站在线观看| 中国女人做爰A片| 亚洲av另类在线观看| 在线资源av-超碰中文在线-成人AV| 九九Av| A A色色| 午夜婷婷久久| 国产精品国产成人国产三级| 有码一区二区三区| 色狠狠综合网| 午夜爱爱爱成人| 可以免费观看的av| 婷婷五月丁香基地| 91九色超碰| 8区视频在线| 色五月 激情婷婷 综合五月天| 美国色五月天婷婷资源站| 97超碰人人操| 国产免费性爱| 91精品国产日韩91久久久久久国模| 婷婷四月 成人 狠狠干| 亚洲A色| 色婷婷亚洲婷婷| 91九色最新视频| 性色婷婷| 六月丁香婷婷尤物| 中文字幕丰满人妻无码专区| 粉嫩尤物在线456| 免费人人操| 开心四月婷婷在线色播播| 五月天播播中文字幕| 99re这里只有| 免费99色| 天天噜天天爱| 在线观看免费狠狠色丁香香综合| 麻豆一区二区免费播放网站| 日本乱论99| 天天射射夜| www.婷婷.com| 思思re最新视频| 九色PORNY自拍成人精彩视频| 久久这里只有精品16| 9l视频自拍九色9l视频自拍九色9l社区| 99在线精品观看99| 免费婷婷| 五月天婷婷婷| 99国产精品久久久久久久久久久| 97婷婷丁香五月天激情图片| 婷婷瑟五月天久久综合| 欧美99视频| 亚洲综合色成丁香五月色| 久热超碰| 色综合五月| yazhoujiqingav| 久久婷婷五月天激情新地址| 日日杆天天| 国产激情久久久| 丁香婷婷大香蕉| 九九热最新| 日本色色影片| 丁香婷婷精品视频| 色色色婷| 91操女| 99热这里有精品24| 99亚州综合精品成人网| 激情宗合 激情宗合| 天天舔天天摸天天透| 另类小说五月天| 婷婷少妇激情| 色在线视频网2025| 91精品综合久久久久久五月丁香| WWW久久久| 日日干夜夜干| 亚洲AV成人精品网站在线播放| 五月花婷婷在线精品视频| 91婷婷丁香五月| 奇米四色五月天| 99热这里只是精品| 高清一区二区三区日本久| 99操中文视频| 亚洲精品婷婷| 婷婷五月另类网站| 激情五月天网页| 色狠狠色噜噜AV天堂五区| 婷婷综合色色| 久久婷婷综合国产| 亚州色综合| 欧美VA在线观看| 99热18| 婷婷五月蜜桃成人桃色丁香| 91久久综合亚洲鲁鲁五月天| 久久网婷婷| 色五月婷婷激情| 久99婷婷色综合| 人妻在线中文字幕久久| 天堂成人A片永久免费网站| 色人久久| 久久久8| 狠狠爱婷婷爱| 97碰人人操| 人人干人人操外国| 天天综合在线网| 婷婷激情五月天桃花网| 天天色综合网吨吧| 桃色五月婷婷| 丁香激情六月天婷婷| 日韩欧美视频一区| 丁香五月婷婷亚洲综合精品在线| AV性爱在线| 激情婷婷五月久久| 色激情五月| 激情网第九色| 婷婷丁香十月| 欧美日本VA| 五月激情站| 热99精品视频在线观看| 99这里只有精| 做爱夜夜干天天操| 亚洲亚洲人成综合网络| 激情丁香婷婷五月天| 97色吧| 天堂五月婷婷| 国产 亚洲 中文在线 字幕| 亚洲综合五月天综合| 天天弄天天操| 日日做天天操夜夜爽| 激情五月丁香五月色| 五月激情网综合| 国产欧美大香蕉一区| 婷婷五月天视频亚洲| 97人人看一| 97在线/亚洲| 九九热超碰| 少妇人妻偷人精品无码视频新浪| 思思久久精品视频| 99精品在线观看| 天天舔天天摸天天透| 免费观看日韩成人av| 九九无码| 五月婷婷色综图片| 婷婷婷婷婷婷婷五月丁香| 成人天天爽| 五月婷色| 九九性视频| 色婷久| 九九色综合| 高清无码 一区 二区 三区| 九九日伊人| 亚洲另类噜噜| 激情五月狠狠喔| 热99免费在线| 色五月婷婷中文字幕在线观看 | 久久精品国产一区二区三区四区| 天天舔天天| 欧在线一区| 五月精品| 色色丁香| 精品 在线 视频 亚洲| 婷婷五月天欧美图片在线播放电驴| 久久全色| 91超碰在线观看| 丁香五月自拍| 成人五月丁香花| 色色色热热热| 黄色三级日本| 日韩一本在线| 婷婷五月激情天| 97视频91| 亚洲一区在线播放| 狠狠xx| 婷婷综合色色| 丁香五月社区| 婷婷五月天小说| 大香蕉久久| 日韩国产在线精品| 五月婷婷婷丁香播| 综合激情伊人影视在线| 国产一区二区av免费| 丁香五月婷婷五月| 五月婷婷综合网| 激情综合网激情五月俺也去| 无套内谢少妇毛片A片樱花| 人妻狠狠操| 校园春色亚洲色| 九九综合伊人| 色婷婷伦理| av成人在线播放| 国产日韩欧美| 99精品大片| 亚洲色五月| 日韩啪啪视品| 日日噜狠狠| 天天影视色综合网| 久久婷婷五月综合伊人| 久久久爱毛片一区二区三区| 深爱五月天| 高清无码 一区 二区 三区| 日韩无码性爱| 性做久久久久久久免费看| 五月天婷婷激情在线色图| 伊人久久婷婷| 超碰成人影视| 久久久免费精彩视频| 激情婷婷啪啪| 亚洲性天天| 婷婷丁香视频| 久久免费精品高清麻豆| 色五月激情五月| 婷婷丁香久久五月综合| 九九精品亚洲| 婷婷五月天色| 激情九月综合| 久久久久久五月天| 色婷婷视频| 婷久久综合| 日韩精品人妻AV一区二区三区| 九九无码| 天天射影视综合网| 亚洲激情综合网| 在线播放 精品| 少妇性按摩无码中文A片| 中文av网| 日日干夜夜撸夜夜骑| 五月丁香激情婷婷| www.lchjjc.com| 婷婷另类小说| 超碰高清在线| 五月婷婷激情四月| 9.1综合网| www.日本91| 91九色PORNY肉丝在线| 丁香五月偷拍| av在线免费网站 | m色激情网| 色色综合成人网| 色爱99| 激情精品久久| 久久久99精品免费观看| 情色婷婷五月天| 五月天另类图片| 五月婷婷天天色| 久99久视频| 亚洲黄3级片网站欧美| 五月婷婷丁香日韩在线| 202丰满熟女妇大| 婷婷黄色| www.天天干| 五月天欧美激情| 精品99久久久久成人网站免费| 五月婷婷激情性爱| 99小视频在线观看| 久久五月婷综合| 最近中文字幕大全免费版在线| 五月丁香婷婷在线| 五月丁香婷婷综合| 五月婷婷久| 五月综合亚洲婷婷| 五月婷婷内射网| 色区久久| 激情黄色小说色五月| WWW·色色色·COM| 婷婷丁香黄色| 果冻传媒A片一二三区| 91婷婷五月天嫩女|