国产精品揄拍一区二区久久,国产高清欧美亚洲,成?V人片一区二区三区久久,小欢喜免费观看,日韩欧美亚洲中文字幕一区二区,亚洲精品欧美日本中文字幕,国产乱人伦偷精品视频免观看,国产欧美亚洲精品久久久,国产99精品一区二区三区

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
久久久久伊人| 日韩两人性爱免费视频| 邻居少妇张开双腿让我爽一夜| aaa国产| 在线观看av的网站| 麻豆国产视频| 国产成人无码www免费视频播放| 91久久精品国产91性色tv| 国产精品交换| 欧美www视频| 东北亲子乱子伦视频| 国产91视频网站| 玖玖在线| 97伊人| 欧美性爱99| 久久久久亚洲AV无码专区首护士| 国产精品一二三产区m553小说 | 国产精品一区二区无码免费看片| 国产毛多水多做爰爽爽爽| 国产农村妇女精品一二区| 2019中文视频免费播放| 黄色免费无码视频网站| 久久久久国产一级毛片高清版| 久久久黄色| 古代黄色一级视频| 国产精品久久AV无码| 精品少妇一区二区三区免费观| 99热免费观看| 一区二区三区偷拍| 色色人妻| 91久久久久国产一区二区| 伊人成人在线观看| 国产成人精品在线| 欧美性爱人人| 91精品国产一级毛片国语版| 国产老熟女一区二区三区| 在线中文字幕视频| 老女人毛片| 暗交老女一区二区三区| 欧美一级a一级a爰片免费免免| 潮喷视频在线| 欧美成人精品一区二区男人看 | 毛片毛片毛片| 国产精品一二三产区m553小说 | 亚洲国产精品久久人人爱潘金莲| 99视频在线看| 无码人妻一区二区三区在线| 中文字幕一区二区三区不卡在线 | 一起草无码在线| 精品无码黑人又粗又大又长| jazzjazz国产精品麻豆| 亚洲天堂AV网| 99re国产| 精品无码人妻一区二区免费蜜桃| 亚洲精品色午夜无码专区日韩| 98年欧美综合性爱| 欧美性受XXXX黑人XYX性爽| 色哟哟国产| 99热国产在线| 欧美性爱乱伦| 99中文字幕| 超碰偷拍| 亚洲人人操| 国产69精品久久久久777| 国产精品91在线| 亚洲欧美日韩电影| 奶大灬好大灬好硬灬好爽在线播放| 国产成a人亚洲精品无码久久| 黄色片无码| 国产精品亚洲天堂| 国内精品视频在线观看| 91亚洲强奸| 欧美在线一区二区| 色哟哟日韩精品| 关之琳| 在线播放高清无码| 拳交美女A片大全| 丁香无码| 国产精品入口| 色婷婷视频| 人妻少妇中文字幕| 国产AV一级片| 午夜人妻理伦影片| 91亚洲精品| 无码人妻少妇| 国产一区二区三区| 亚洲无码少妇| 精品国产网站| 狼友91精品一区二区三区| 亚洲一区二区自拍| 免费操b视频| 交视频在线播放| 在线看片免费人成视频免费大片| 老女人毛片| 五月婷婷视频在线观看| 97视频在线| 欧美色色网| 国产一区二区三区三州| 暗交老女一区二区三区| 高清无码免费观看| 在线观看色| 午夜福利| 亚洲高清一区二区三区| 无码人妻一区二区三区在线视频| 国产高清精品软件| 国产无套内精一级毛片| 中文字幕第四页| 国产特级片| 精品国产免费无码久久久| 日韩久久人妻| 免费国产精品视频| 亚洲婷婷五月天| 在线观看色| 91精品一区二区三区久久久久久| 在线视频自拍| 久久专区| 国产精品无码一区二区桃花视频| 一级a毛片免费观看久久精品| 国产高清无码一区| a级无码毛片| 欧美午夜伦理| 人人综合| 99精品人妻一二三区| 欧美大片一区二区| 久久久内射| 日本国产精品无码一区久久下载| 日韩福利片| 无码精品久久| 91精品视频国产| 欧美成人一区三区无码乱码A片 | 久操视频在线| 99视频在线| 影音先锋男人在线| 18禁免费看| 亚洲人成色无码yyyy| 荫蒂添的好舒服视频囗交| 视频一区在线| 国产在线观看精品| 日本乱伦网站| 亚洲一本色道中文无码aV天美| 欧美一区二区三区视频在线观看| 日韩av中文字幕在线| 欧洲AV一区二区三区| 天天激情| 91国内产香蕉| 亚洲中文在线观看| 国产一级片免费| 久久国产V一级毛多内射| 天天干伊人久久| 亚洲精品国产AV| 91色逼资源| 日韩无码内射| 国产伦精品一级二级三级妓女| 毛片无码一区二区三区A片视频| 国产无码福利导航| 婷婷一区二区| 久久久免费观看| 黄色美女网站| 色一情一乱一伦| 亚洲人妻| 免费黄色大片| 中文字幕AV在线| 久久精品成人一区二区三区蜜臀| 日韩丰满少妇无码内射| 国产爽爽爽| 亚洲国产永久7777kkk| 色欲日韩欧美亚洲| 丁香五月天激情| 91睡熟迷奷系列精品| TS人妖另类精品视频系列| 国产大屁股喷水视频在线观看| 亚洲熟女乱综合一区二区三区| 亚洲AV成人无码精电影在线| 日韩久久影视| 国产A自拍| 毛片小视频| 免费一级A片| 在线观看a片| 国产精品亚洲一区二区无码| 少妇精品放荡导航| 啪啪导航| 波多野结衣在线视频观看 | 91人妻无码一区二区久久| 91久久精品国产91性色tv| 日韩欧美一区二区三区在线观看| 97午夜福利| 精品自拍AV| 天天爱综合| 亚洲无码国产精品| 99人人操| 小俊┅┅快┅┅用力啊| 国产欧美日韩一区| 一级特色黄大片| 精品成人在线| 国产夫妻av| 国产亚洲色婷婷久久99精品91| 自拍偷拍网站| 被男人疯狂揉吃奶胸视频| 国产精品女主播一区二区三区| 无码精品久久久久久亚洲| 国产三级网站| 国产精品久久久久久久黄无码| 天天干天天弄| 亚洲福利视频一区| 欧洲精品一区| 草榴在线视频| 国产精品自拍一区| 欧美午夜激情| 久久精品日韩| 男人的天堂在线视频| 国产一区二区三区无码| 超碰100| 国产制服丝袜在线| 最新超碰| 国产免费一区二区三区最新不卡| 五月婷婷丁香六月| a v最新天堂| 国产无码www| 中文人妻av久久人妻18| 超碰97在线免费观看| 日韩毛片在线| 国产精品综合| 99亚洲精品| 亚洲欧洲一区| 亚洲国产成人精品久久| 精品亚洲AV乱码国产毛片| 自拍偷在线精品自拍偷无码专区| 日韩三级片免费观看| 嫩草在线观看| 亚洲小电影| 国产在线91| 免费激情网站| 国产中文字幕视频| japan极品人妻videos| 99亚洲精品| 综合色天天| 综合无码| 熟女乱一区二区三区四区 | 亚欧高清无码| 久久AV高潮AV无码AV喷吹| 精品人妻伦一二三区久久斗罗| 国产精品免费在线| 天天操夜夜操| 国产裸体美女永久免费无遮挡| 色综合天天综合网天天狠天天 | 亚洲黄片免费看| 免费国产视频| 欧美中出| 欧美α片在线播放| 日本熟女视频| 精品国产自在精品国产精小说| 99人妻| 国产女人18毛片水真多18精品 | 一区二区视频免费观看| 午夜天堂精品| 亚洲无码网址| 欧美人成在线| 国产AV视屏| 日韩毛片免费视频一级特黄| 性做久久久久久久| 51ⅴ精品国产91久久久久久| 91久久国产综合久久91精品网站 | 国产精品区在线观看| 高潮毛片无遮挡免费高清无码| 国产精品嫩草影院CCm| 无码一区二| 琪琪午夜成人久久电影网| 欧美小黄片| 国产精品第5页| 欧美午夜视频| 中文字幕一二区| 久久久久无码国产精品Sm高潮| 99Reav| 韩国无码视频| 亚洲精品一区二三区不卡| 亚洲成人精品久久| 一级伦奷片高潮无码看了5| 宅男午夜影院| 一区二区三区在线播放| 日本人妻中文字幕| 亚洲香蕉在线观看| 日日夜夜精品| 中文字幕一级| 精品自拍视频| 久久久黄色| AV片在线观看| 国产黄色片在线观看| 精品国产乱码久久久久久1区2区| 国产亚洲欧美一区二区三区| 国产日韩视频在线观看| 精品欧美一区二区久久久| 草莓视频在线| 色中文字幕| 丁香五月在线| 九九精品免费视频| 亚洲成人一区| 久久亚洲一区二区三区四区| 一区二区三区国产精品| 一区二区三区日韩精品| 黑人免费福利视频| 三级片在线播放网站| 亚洲三级在线视频| 亚洲欧美日韩综合| 伊人三区| 黄色成年网站| 狠狠精品干练久久久无码中文字幕| 国产精品一区二区三区AV | 不卡无码AV| 黄色精品视频在线观看| 久久成人国产| 偷拍亚洲欧美| 欧美一区二区在线| 丰满人妻妇伦又伦精品APP | 国产精品性爱视频| 亚洲午夜AV久久乱码| 国产1区2区3区| 开心激情综合| 久久久一区二区三区四区| 中文字幕丝袜| 国产高清免费在线| 中文字幕无码在线观看| 欧美在线视频一区| 久久久欧韩成人看片| 小泽玛利亚在线观看| 十八禁视频网站| 一级黄片无码| 日韩91| 亚洲激情一区二区| 国产精品小电影| 欧美极品欧美精品欧美图片 | 在线观看亚洲AV| AV在线导航| 久久久精品亚洲| 成人短视频在线观看| 无码aⅴ精品日本无码久久| 精品一区二区免费| 久久96国产精品久久99软件| 黄色三级网站| 国产人妖| 久久午夜免费视频| 国产91精品一区二区| 国产精品久久久久久中文字| 香蕉视频一区二区三区| 无码国产精品一区二区免费网站| 无码人妻精品一区二区三区夜夜嗨| 性一交—乱一性一A片在线播放| 国产毛片在线看| 超碰在线导航| 欧美日韩在线一区二区| 亚洲无码精品一区| 亚洲伦理一区二区| av色天堂| 欧美喷潮视频| 日韩亚洲一区二区| 久久精品国产一区二区三区| 日韩成人免费视频| 超碰熟妇| 久久久久久久久影院| 精品久久BBBBB精品人妻 | 久久久精品亚洲| 黄色免费网站在线观看| 国产精品久久国产精品| 日韩精品久久久久久久的张开腿让| 久久99国产综合精品免费| 久久性爱视频| 午夜精品一区二区三区在线视频| 全黄一级毛片免费| 国产精品精品| 日韩欧美精品| 另类小说第一页| 亚洲综合视频| 日韩无码| 偷拍洗澡一区二区三区| 国产精品亚洲天堂| 日韩无码影片| 日韩欧美视频在线| 国产黄片观看| av免费观看网站| 性爱一区二区三区| 久久久久无码精品国产电影| 一级特黄aa大片欧美| 国产人妻人伦精品1国产盗摄| 影音先锋男人在线| 在线无码视频| 国产精品一区二区在线| 日韩二级片| 色黄大色黄女片免费看直播| 黄色AV免费看| 日韩黄色片在线观看| 亚洲欧美在线观看| 国产成人一区二区| 免费观看av网站| 91无码人妻精品一区二区| 婷婷无码视频| 日韩丰满人妻性爱| 精品国产日韩亚洲| 久久黄色电影网站| 国产精品一区二区三| 乱伦五月天| 三级片在线观看网址| 成全视频观看免费高清第6季| 99大香蕉| 三级黄在线观看| 日韩无码P| 天天中文激情字幕| 丰满肥臀无码一区二区三区| 国产日韩免费| 天天拍天天干| 国产精品久久久久永久免费看| 日韩久久无码视频| 午夜黄色一级片| 国产成人精品自拍| 国产A视频| 国产精品一级二级三级| 日日夜夜视频| 国产熟女AV| 五月丁香五月婷婷| 中文字幕久久精品无码综合网| 免费a视频| 精品人妻中文字幕| 欧美无线码| 26uuu欧美| 日韩无码高清视频| 99国产精品久久久久久久久久久| 高清无码啪啪| 成人午夜毛片| 亚洲国产欧美日韩在线观看第一区 | 天天操导航| 天天综合天天| 亚洲第一黄片| 调教拨开两唇打花蒂戒尺| 啪啪午夜免费视频| 人妻内射一区二区在线视频| 青青在线视频| 日韩强奸乱伦Av| 欧美一区二区丁香五月天激情 | 在线观看网站深夜免费| 国产一级一区| 欧美日韩免费| 国产精品成人AAAA网站女吊丝| 国产激情无码| 亚洲精品在线视频| 国产123视频| 91综合在线| 欧美日韩一二| 色综合色综合| 成人福利视频导航| 加勒比在线视频| 暗交老女一区二区三区| 中文字幕无码在线| 一区手机福利视频导航| 久久人人爽爽人人爽人人片av| 日韩成人无码| 国产精品一级二级三级| 婷婷婷月天| 尤物在线| 久久久久国产精品免费免费搜索| 久久国产精品偷| 国产精品久久一区二区三影音先锋| 强奸乱伦视频第二页| 婷婷一区二区| 国产精品IGAO视频网网址| 欧美交资源www网站| 乱女乱妇熟女熟妇综合网站| 99久久久无码国产精品无卡| 日韩一区二区无码| 婷婷九月色| 国产一级自拍| 国产视频99| 91激情视频| 亚洲伦理一区二区| 涩综合导航| 日韩久久影院| 亚洲无码一区在线| 国产丝袜视频在线观看| 成人7777| 成人蜜乳av| 亚洲无码一二三区| 日批视频网站| 日韩久久精品| 欧美一区二区在线播放| 人人妻超碰| 中文字幕亚洲天堂| www天堂网极品| 水蜜桃久久| 国产精品高潮呻吟久久| 国产另类视频| 国产亲子乱露脸一区二区| 欧美黄片在线看| 91视频网站| 国产黄色在线| 丰满少妇被猛烈进入| 免费黄色视屏| 久久黄色大片| 无码人妻精品一区二区中文| 91这里只有精品| 超碰在线观看91| 久久久国产熟女一区二区三区| 国产老熟女一区二区三区仙踪密林| 国产午夜小视频| 亚洲视频www| 亚洲国产精品自拍| 人与禽性视频77777| 欧美日韩综合视频| 国产免费视屏| 日韩精品第一页| 中文字幕精品无码| 久久精品国产亚洲A| 久久免费无码视频| 欧美第九页| 丁香婷婷五月| 91精品在线播放| 线观看免费完整aaa| 青娱乐自拍偷拍| 无码不卡一区二区| 熟女一区| 少妇AV一区二区三区无码按摩| 无码国产一区二区| 99在线视频精品| 人妻丰满熟妇无码区免费| 日日夜夜草| 国产精品国产三级国产专业不| 欧美三级午夜理伦三级中视频| 国产精品久久久久久久久久10秀| 欧美激情乱伦| 亚洲黄色一区二区| 久久亚洲一区二区| 嫩草国产| 亚洲天堂资源| 欧美日本一本| 亚洲第一黄色| 97视频在线| 国产视频手机在线| 狼人综合网| 一级a一级a爰片免费免免水网| 饱满福利导航| 久久中文视频| 国产亚洲精品久久久久久牛牛 | 亚洲一区二区三区高清| 免费高清无码| 欧美一级特黄片| 成人欧美一区二区三区黑人孕妇| 又粗又硬视频| 精品无码在线观看| 日韩免费高清| 狠狠做深爱婷婷综合一区| 国产欧美日韩精品专区黑人| 一区二区在线视频观看| 高清无码专区| 三级片妖精视频| 日本超碰| 精品久久BBBBB精品人妻| 91久6| 人妻无码熟妇乱又视频| 91口爆吞精国产对白| 91精品国产| 日韩不卡一区| 成年人在线视频| 国产一级A片在线观看免费视频| 亚洲无码天堂| 日本中文字幕在线播放| 久久久久无码国产精品Sm高潮| 日韩精品无码一区二区河北彩花| 懂色一区二区三区久久久 | 亚洲精品黄片| AV网站久久| 久久久18禁一区二区三区精品| 亚洲黄网在线观看| 亚洲精彩视频在线观看| 亚洲黑人Av| 无套内射在线观看| 国产欧美日韩在线观看| 日本护士高潮乱喷www| 日韩精品在线观看免费| 欧美老少交| 久久久一级| 亚洲精品三级| 免费毛片网址| 搡老女人老91妇女老熟女| 国产欧美日韩一区二区三区| 国产精品久久久久久久久久直播| 亚洲巨爆乳一区二区三区四季网| 乱熟女高潮一区二区在线| 天天干视频| 日韩一区欧美| 免费人人操网| 日韩免费操逼视频| 国产午夜一区二区| 成人午夜毛片| 91大神网址| 欧美日韩三级视频| 欧美一区二区在线播放| yellow视频在线观看| 天天日天天射天天操| 久久香蕉av| 日日噜噜噜| 一本一道久久a久久精品综合蜜臀 国产精品久久久久久久久无码ⅴa | 亚洲激情图片| 激情一区二区| 91久久偷偷做嫩草影院| 中文制服丝袜熟女AV亚洲| 福利导航站| 人妻夜夜爽天天爽| 国产无码自拍| 污污污免费网站| 成人在线性爱免费视频| 久久99精品久久久久久园产越南| 中文字幕人妻在线| 国产精品农村妇女AAAA| 国产丨熟女丨国产熟女| 欧美午夜三级| 秋霞视频在线观看| 精品久久久久中文慕人妻| 人人摸人人上人人| 91免费看视频| 国产人成一区二区三区影院| 亚洲人人夜夜澡人人爽| 五月丁香中文字幕| 亚洲黄视频| 日韩不卡毛片| 超碰黄色| 国产精品羞羞无码久久久| 国产黄在线| 国产无码毛片| 国产精品一区二区在线| 野外欧美性爱无码| 国产日韩欧美一区二区东京热 | 亚洲国产精一区二区三区性色| 五月天激情丝袜网站| 国产又爽又黄无码无遮挡在线观看| 人妻系列孕妇篇| 国产视频资源| 最新亚洲中文字幕| 日韩午夜福利片| 国产精品欧美性爱| 精品伊人| 在线一区二区视频| 美女18禁网站| 午夜寂寞院| 亚洲视频在线观看| 日韩一区欧美| 激情丁香五月| 国产精品久久久久无码AV绿帽男| 国产jizz| 国产日韩精品无码区免费专区国产| 免费一级黄色大片| 成人精品视频| 日韩无码看片| 久久久91人妻无码| 亚洲大片免费看| 四虎在线观看| 亚洲一级AV无码毛片| 五月天综合| 黄色国产在线观看| 中文字幕无码一区二区三区一本久| 亚洲一级AV无码毛片| 亚洲无线观看| 欧美高清HD18日本| 人妻99| 国产精品一区二区三区四区| 国产女同互慰在线观看| 日韩欧美视频一区二区| 国产高清黄片| 国产精品不卡| 午夜视频网站在线观看| 亚洲天堂影院| 一区二区三区日韩欧美| AV中文在线播放| 日韩国产精品一级毛片在线| 青青草成人网| 日本特黄特色aaa大片免费| 久久只有精品| 黄色在线观看国产| 亚洲九九九| 国产一级a毛一级a看免费软件| 啪啪一区二区| 91麻豆精品国产91久久久无需广告| 欧美日韩黄| 激情欧美一区二区三区中文字幕| 老熟妇仑乱一区二区av| 欧美多毛熟妇| 无码中文字幕在线观看| 少妇无套内谢久久久久| 伊人三区| 日日操夜夜爽| 亚洲精品成人网站| 国产性―交―乱―色―情人| 国产又粗又长又深又黑又硬| 日韩综合网| 国产又黄又猛又爽| 欧美一级A片高清免费播放| 91在线视频观看| 国产成人三区| 日韩欧美在线视频| 日本黄色A片| 人人爱人人操人人摸| 亚洲精品国产一区二区三区三州4点| 国产综合内射日韩久| 97久久精品| 69精品一区二区三区无码吞精| www欧美在线| 欧美日韩国产一区| 91网页版| 9l视频自拍蝌蚪9l视频成人| 上国产操逼网| 国产乡下妇女做爰| 国产成人无码一区二区在线观看| 成年人在线视频| 久久国产毛片| 91在线视频| 欧美操逼网址| av天堂一区| 国产精品乱码一区二区| 一区二区在线观看视频| 九九精品视频在线观看| 国产毛多水多做爰爽爽爽| 久久国产高清视频| 国产免费自拍视频| 三级片中文字幕在线观看| 色先锋资源| 亚洲国产激情| 午夜成人亚洲理伦片在线观看| 欧美第一区| 久久91精品国产91久久跳| 欧美日韩一级黄片| 欧美天堂在线观看| 疯狂操逼亚洲| 秋霞午夜一区二区三区视频| 一本大道无码| 国产一级视频| 视频一区二区在线观看| 国产午夜av| 国产又粗又大又爽视频| 欧美日韩精品一区二区| 久久精品国产精品| 亚洲国产欧美日韩在线观看第一区 | 最好看的2018中文2019| 99久久国产视频| 九九热在线观看| 亚洲系列第一页| 日本丰满熟女视频中文字幕| 色欲日韩精品在线| 黄片视频大全免费看| 国产午夜精品在线| av资源网址| 精品视频国产| 波多无码中出| 五月天乱伦视频| 欧美日韩无码精品| 嫩草影院一区二区| 久久国产露脸精品国产| 99精品国产乱码久久久人妻| 伊人成人电影| 欧美日韩高清丝袜| 又粗又大又爽| 国产精品一区二区电影| 久久麻豆| 日日躁夜夜躁| 青草视频在线| 一区二区三区四区免费视频| 精品一区二区久久| 亚洲无码成人网站| 乱伦熟女女网| 日韩欧美久久| 成年人毛片| 伦乱视频| 国产日韩精品无码区免费专区国产| 亚洲人妻| 国产强奸乱伦视频免费| 日韩三级免费观看| 在线观看欧美日韩视频| 黄色片网站在线观看| 国产精品第1页| 这里都是精品| 美女网站黄| 国产男女猛烈无遮掩视频免费网站| 欧洲亚洲精品| 国产成人精品在线观看| 成人无码视频在线观看| 福利一区二区视频| 无码精品一区| 国产黄色片免费| 五月婷婷av| 国产高清无码在线观看| 国产精品999久久久| 夜夜天天干| 朝桐光一区二区三区| 欧美日韩视频在线| 苍井空无码在线观看| 亚洲熟妇无码AV无码| 一色综合| 日韩在线观看AV| 久久99久久99精品免观看软件| 久久精品久久久久久久| 2024AV天堂| 国产一级特黄大片色| 欧美婷婷| 亚洲无码午夜福利| 国产免费一区| 亚洲国产精品一区二区久久恐怖片| 欧美综合在线观看| 日韩成人免费在线| 一级性视频| 国精产品一区一区三区四区| 人人摸免费视| 国产日产久久高清欧美一区| 欧美视频第一页| 日韩一道本视频| 国产一级做a爰片久久毛片男| 国产一区二区三区免费观看| 午夜国产在线观看| 天天爽夜夜爽| 黄色三级片在线观看| 午夜成人免费无码A片| 中文字幕精品一区| 国产人妻无码一区二区三区不卡| 亚洲强奸视频网站| 亚洲黄色在线观看| 91精品视频在线播放| 久久免费视频6| 亚洲国产精品无码久久久久久久久| 美女色色视频网站| 欧美大成色www永久网站婷| 亚洲综合国产| 免费看黄在线观看| 无码一级毛片| 女人弄爽到高潮免费视频网站| 精品人妻一区二区三区日产乱码| 人妖AV| 日本三级不卡| 激情图片小说| 午夜天堂一区二区三区| 免费操逼| 久久99精品久久久久| 免费一级大片| 日本午夜在线| 久久久一区二区三区| 欧美高清一区二区| 国产欧美精品一区二区| 一级性爱电影在线观看| 国产精品美女www爽爽爽视频| 尤物网在线观看| 高清无码片| 欧美日韩国产精品一区二区| 国产精品久久一区二区三区| 亚洲Av永久无码精品国产精品| 国产精品福利在线| 国产成人三区| 欧美秋霞| 热久久91| 亚洲欧洲天堂| 国产丰满乱子伦无码| 欧美肏屄视频| jazzjazz国产精品麻豆 | 久久久国产精品| 无码资源在线| 久久久久久国产精品| 天天插天天日| 一级操逼毛片| 色色色影院| 欧美一二三区| 国产一级免费av| 国产激情无码| 一级片在线观看视频| 色丁香五月婷婷| 乱伦av中文字幕| 亚洲AV无线在线观看| 三级片在线观看视频| 成年免费视频黄网站在线观看| 国产三级91| 日韩小电影| 亚洲中文字幕乱码无码一区二区| 国产一区电影| 成午夜精品一区二区三区软件| 91高清国产| 凹凸视频熟女一区二区| 凹凸视频在线| 99精品久久毛片A片| 国产成人无码www免费视频播放| 伊人三区| 码人妻免费视频| 亚洲一区二区免费| 91popn.com在线生产| 国产在线视频第一页| 精品视频免费| 婷婷精品视频| 国产免费不卡| 无码国产精品一区二区色情八戒| 黄色三级在线观看| 国产精品91在线| αⅴ天堂αⅴ| 91精品夜夜夜一区二区| 99精品成人无码A片观看金桔| 国产精品成人亚洲一区二区| 人人操人人搞97| 国产精品午夜福利视频| 安徽妇搡bbbb搡bbbb按摩| 熟妇人妻一区二区三区四区| 黄色免费网站在线观看| 国产精品久久久久无码AV绿帽男| 欧美a在线| 亚洲欧美乱伦| 亚洲国产精品无码久久久秋霞1| 黄色免费看网站| 一本久道久久| 日本精品一区| 精品欧美黑人一区二区三区| 97人妻人人澡人人爽人人精品| 国产免费www| 黄色黄片免费看| 亚洲熟人妇一区二区三区| 性爱一区| 国产精品tv| 国产欧美日本| 国产在线无码视频| 欧美喷潮视频| 高清黄色无码| 欧美无砖砖区免费| 午夜久久无码成人免费AV麻豆婷| 色欲AV无码精品一区二区久久| 久热精品在线| 麻豆久久| 婷婷久久综合| 亚洲图片在线观看| 国产精品一区在线| 欧美日韩综合| av黄色| 国产精品小电影|