日韩欧美?v视频在线观看-亚洲无码一二专区-国产超碰精久久久久久无码?v-欧美日韩人妻精品一区二区在线播放-亚洲日韩中文字幕乱码在线看-国产99久久亚洲综合精品-日韩在线看片免费观看-无码精品尤物一区二区三区

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
2020无码| 欧美群妇大交群| 欧美熟妇精品一区二区蜜桃视频 | 成人国产色情无码视频网站代码 | 毛片毛片毛片| 视频在线无码| 国产精品成人亚洲一区二区| 欧美一区二| 国产精品无码在线观看| 色姑娘综合网| 国产极品在线观看| 日韩欧美精品在线| 国产思思| 精品久久国产| 91视频导航| 日韩一级黄色大片| 免费三级网站| 久久欧美国产伦子伦精品按摩| 久久精品国产亚洲av瑜伽仙踪林| 特黄AAAAAAA片免费视频| 精品无码久久久久久久久成人| 日韩无码性爱视频| 亚洲图片欧美视频| 人人操人人早| 国产69精品久久久久孕妇大杂乱| 阿v天堂2014| 亚洲国产福利| 91少妇被爽到高潮喷| 午夜久久无码成人免费AV麻豆婷| 久久久精| 国产毛片毛片毛片| 一级黄色录像片| 国产精品一区二区在线免费观看 | 无码高清电影| 欧美小视频在线观看| 日本一区二区三区四区| 伊人久久综合| 国产人妻精品无码免费| 国产女人性拳交| 日韩无码一区二区| 黄网站入口| 九九九国产| 超碰97人妻| 91在线公开视频| 最新天堂AV| 国产激情视频在线| 真实乱偷全部视频| 日韩久久久久久| 伊人青青草| 亚洲福利网| 久久精品国产亚洲AV超碰| 高清无码视频在线看| 高清无码免费观看| 国产午夜精品无码理伦片| 国色天香一区二区| 国产A级片| 国产精品第1页| 亚洲黄色网址| 男人资源站| 懂色av蜜臀av粉嫩av分享吧| 黄色网在线看| 囯产伦精一区二区三区妓| 99色在线视频| 久久久久亚洲精品国产| 动漫精品无码| 日韩精品综合| 91视频国产精品| 高清无码免费| 超碰首页| 最新在线中文字幕| 欧美性猛交99久久久久99按摩| 午夜欧美巨大性欧美巨大| 性爱黄色亚洲| 久久艹艹艹| 91囯在线啪无码| 91插插插永久免费| 国产性爱网| 国产精品一级二级三级| 这里只有精品在线| 99自拍视频| 欧洲精品视频在线观看| 精品少妇一区二区三区日产乱码| 婷婷97狠狠成人网站| 中文字幕 一区二区三区| 这里都是精品| 美女直播全婐APP免费| 呻吟 玩弄 翻搅 花蒂 肿大| 久99综合婷婷| 国产影视久久久| 国产口爆| 影音先锋男人在线| 亚洲第一天堂网| 日韩欧美偷拍| 亚洲免费成人| 欧美一二三四| 91精品国自产在线偷拍蜜桃| 污网站在线观看| 国产精品一区二区不卡| 欧美性爱视频在线播放| 国产精品羞羞无码久久久| 九九人妻| 日韩欧美在线播放| 亚洲国产影院| 午夜福利国产| 色综合天天综合网国产成人网| 在线一区二区三区| av一级毛片| 国产女人爽到高潮a毛片| 欧洲av无码| 色吧色吧色吧| 91狠狠| 国产高清无码在线观看| 日本亚洲一区| 五月天乱伦视频| 亚洲一级网站| 无码精品A∨在线观看无| 精品一区二区久久久久久无码| 国产福利一区二区三区视频| 欧美日韩国产中文字幕| 乱子轮熟睡1区| AV天堂亚洲无码| 亚洲一区欧美一区| 人妻一二三区| 久久精品久久久久久久| 亚洲精品无码一区二区电影 | 色情乱伦av| 亚洲无码1区2区3区| 淫荡网站| 国产一级a爱做片免费☆观看| 国产精品精品| 中韩XXX抄逼| 成全视频观看免费高清第6季| 亚洲国产欧美日韩在线观看第一区 | 99久久久久久| 懂色Av噜噜一区二区三区AV| 亚洲一区av| 亚洲女人av久久天堂| 伊人剧场91| 一级黄色电影免费看| 久久亚洲国产精品无码一区| 久久黄色小视频| 波多野结衣在线视频观看| 久久久久逼| 精品乱伦一区二区三区| 不卡免费AV| 国产精品一区十二区无码喷水欧美| 性色AV一区二区三区| 黄色亚洲视频| 午夜视频一区二区| 91超碰在线观看| 国产91丝袜在线播放九色| 在线观看你懂得| www,亚洲第一操逼逼| 久久性视频| 成人日韩无码| 国产老女人精品毛片久久| 欧美精品久久久久A片| 狠狠精品干练久久久无码中文字幕| 天天操一操| 五月婷婷色色午夜| jlzzjlzz国产精品久久| 91睡熟迷奷系列精品| 欧美一级黄色大片| 大香蕉综合网| 少妇一级A片在线观看妖精视频| 日韩精品无码熟人妻视频| 一级全黄少妇性色生活片| 午夜性色福利视频| 欧美黄色一级视频| 午夜操逼| 精品人豆妻| 精品人妻无码| 日韩免费在线观看| 欧美亚洲三级| 国产AV一卡二卡| 中文字字幕一区二区三区四区五区 | 亚洲婷婷五月| 性色AV一区二区三区| 18成年网站| 91精品中文字幕| 国产免费小视频| 亚洲国产精品久久| 亚洲免费成人| 天天干青青| 亚洲视频一区| 国产午夜精品一区二区| 91精品国产aⅴ一区二区| 国产精品呻吟久久Av无码| 97自拍视频| 国产黄片一区| 黄色一区二区三区四区| 国产无码www| 亚洲美女毛片| 丝袜乱伦视频| 欧美日韩在线免费观看| 国产jizz| 苍井空无码一区| 激情av乱伦| 在线视频这里只有精品| 欧美三级网站| 国产99热| 99大香蕉| 91精品网站| 亚洲天堂成人网站| 国产伦精品一级二级三级妓女| 一级特黄视频| 青青草97国产精品麻豆| 天天天干干| 欧美呦呦| 国产午夜三级一区二区三| 国产欧美精品| 色悠久久久| 日韩国产欧美一区| 欧美中出| 国产精品女| 伊人久操| 人人操人人摸人人干| 岛国二区| 色色视频网站| 日韩精品5| 国产午夜精品一区| 天天干视频| 欧美日韩日逼| 高清无码操逼视频www| 国产aⅴ| 成人AV一区二区三区无码金桔| 韩国无码一区二区三区精品| 午夜色色视频| 日本人妻一区| 国产91精品一区二区| 成人性生交大片费看中文| 91久久精品国产91久久| 国产精品一区视频| 国产一级黄片| 男女啪啪网址| 日韩欧美午夜| 无码一区二| 超碰影视| 国产精品久久久爽爽爽麻豆色哟哟| 无码在线专区| 欧美视频在线一区| 91AAA在线观看| 四川一级毛片免费观看| 国产女主播一区二区| 搡老熟女老女人一区二区| 日韩精品免费一区二区夜夜嗨 | 丰满人妻一区二区三区免费视频| 久久成人一区二区| 三级少妇| 久久久黄色| AV无码波多野结衣| 国产精品女主播一区二区三区| 91在线看视频| 91亚洲视频| 九九精品在线视频| 久久77| 99国产视频| 欧洲精品码一区二区三区免费看 | 曰韩无码| 91视频网| 国产高清一级毛片在线不卡| 青娱乐自拍偷拍| 国产精品偷伦免费视频| 欧美日韩精品在线| 中文字幕一区二区三区| 91精品国产92久久久久 | 白丝喷白浆一区二区在线观看| 欧美福利视频| 婷婷五月综合激情| 日本免费久久| 亚洲人人操| 日韩精品久久久久久免费| 中文一区| 日韩三级电影在线观看| 国产成人精品在线| 国产三级片一区二区| 影音先锋男人av资源| 亚洲欧洲一区二区三区| 九九九九九九精品| 国产精品久久久久久久久久尿| 色综合天天综合网天天看片| 伊人2222综合| 日韩乱伦一区| 国产又粗又大又黄| 乳色AV| 91福利网| 老司机午夜影院| 国内成人自拍| 无码免费AAAAAAAAA软件| 亚洲免费一区| 成人免费黄色大片| 国产91精品一区二区绿帽| 日本三级视频| 日日干日日射| 97综合| 韩国久久| 久久精品丝袜高跟鞋| 亚洲视频免费| 天天草天天爽| 亚洲无码免费观看| 国产又粗又长又硬| 日韩无码第一页| 2023国产无套免费视频| 毛片免费观看| 一起操网址| 天天插天天射| 色六月婷婷| 在线国产视频| 二区三区无码| 97国产色呦呦呦夜嗨嗨| 四虎免费看黄| 97超人人操| 日日精品| 免费永久黄片| 国产一级片在线| 欧美一区二区在线| 中文字幕精品一二三四五六七八| 亚洲精品电影| 国产乱人偷精品视频| 国产精品久免费的黄网站| 国产精品一级无码免费播放| 久久久婷婷| FREEZEFRAME丰满少妇| 高潮喷水波多野结衣在线观看| 91小视频| 熟妇性爱视频| 天天日天天干天天操| 国产一区二| 丁香五月激情网| 国产操逼综合| 日本黄色小视频| 91精品国产午夜福利在线观看| 亚洲精品无码一区二区四区| 久久午夜夜伦鲁鲁一区二区| AV在线毛片| 成人A片无码水蜜桃免费网站软件| 少妇无套内谢久久久久| 色欲aⅴ入口| 无码一二三区| 亚洲天堂无码| 国产免费观看视频| 999毛片| 成人三级视频| 日韩欧美久久| 红桃视频一区二区三区免费| 黄片无码视频| 一区二区无码在线| 偷拍自拍网| 国产白嫩护士被弄高潮| 国产影视久久久| 国产精品久久久久久一级毛片| 韩国无码成人片在线观看| 午夜精品久久久久久 | 久久久久久91亚洲精品中文字幕| 天天日综合| 99免费观看视频| 伊人五月天综合| 日韩欧美精品在线| 97碰碰碰| 99在线无码精品| 国产激情视频在线播放| 精品国产鲁一鲁一区二区红桃影视 | 色乱av| 日韩成人无码视频| 99热这里| av第一福利导航| 美女黄网| 亚洲一区电影| 欧美一a一片一级一片| 午夜私人天堂| 久久免费无码视频| 国产精品无码粉嫩小泬| 中文字幕黄色| 国产在线综合网站| 91丨九色丨蝌蚪丨少妇在线观看 | 免费一级A毛片夜夜看| 日韩三级亚洲欧美激情| 日韩毛片在线观看| 丁香五月中文字幕| 国产精品久久久久久一级毛片探花| 国产精品一| 中文字幕一区在线播放| 精品人妻视频日韩| 欧美三级三级三级| 精品综合网| 国产九色| 国产高清无码在线观看| 亚洲男人天堂网| 中文字幕第99页| 视频一区在线播放| 老司机精品视频在线| 久久网站导航| 日韩 cbbav| 国产99久久九九精品无码免费| 亚洲成人免费| 国产3级片| 成人一级| 国产欧美日韩在线观看| 91福利视频导航| 亚州国产| 国产精品尤物| 秋霞午夜福利视频| 午夜男人视频| 有没有强奸乱伦免费网站免费网站| a级无码毛片| 精品无码黑人又粗又大又长| 国产好爽又高潮了毛片91| 亚洲影音先锋在线| 嫖老熟女x88AV| 亚洲综合激情| 国产精品a免费一区久久网址| 欧美青青草| 国产伦精品一区二区三区妓女下载| 成 人 免费 黄 色| 色吧在线无码| 乱伦av中文字幕| 国产毛片欧美毛片久久久| 欧美第二页| 亚洲三级网站| 91偷拍一区二区三区精品| 黄色链接在线观看无码| 亚洲黄网在线观看| 人人干人人爽| 午夜操逼| 国产 丝袜 另类 精品 综合| 国产精品免费区二区三区观看四虎 | 黄色小视频在线观看| 成人免费电影网站| 色六月婷婷| 日本精品在线观看| 中文字幕视频一区| 国产二区AV| 久草国产在线| 无码视频专区| 99国产在线观看免费视频| 国产av电影网站| 一本一道久久a久久精品逆3p| 玖玖国产| 黄片免费视频| 91老熟女| 国产成人精品无码一区二区蜜柚| 欧美日韩亚洲国产| 91福利网| 久久无码影视| 成年人在线视频| 乱伦综合网| 久久久精品影视| 久久久国产熟女一区二区三区| 一二三区在线视频| av午夜| 五月丁香综合在线| 国产精品一区二区三区无码 | 日韩精品欧美| 无码三级片视频| 红桃视频一区二区三区免费| 久久精品国产亚洲A| 国产精品电影一区| 超碰在线免费| 人人专区人人操人人| 久久99久国产精品黄毛片入口| 国产9999| 尤物.com| 国产精品a免费一区久久网址| a视频在线观看| 天天插天天色| 日韩无码网| japan极品人妻videos| 欧美熟女一区| 欧美性受XXXX黑人XYX性爽| 久草福利在线视频| 亚洲av无码一区二区三| 日韩一级无码毛片| 亚洲无码视频在线观看| 日韩视频一区二区| 日韩AV专区| 久久国产一区二区深田咏美| 欧美一级黄色大片| 91精品91久久久久77777| 日韩久久电影| 手机在线精品视频| 日本伊人网| 日本护士高潮大叫| 国产在线激情| 亚洲电影在线| 亚洲日韩强奸乱伦| 亚洲一区二区三区视频| 99青青草| 男女激情网站| 亚洲风情第一页| 久久只有精品| 日韩一级无码| 中国女人毛片一级A片| 人妻激情偷乱视频一区二区三区| 中文久久| 中文字幕精品一二三四五六七八| 色婷婷丁香五月| 操欧美老熟女| 久久人妻无码| 日本黄色三级片在线观看| 91亚洲精品国偷拍自产乱码| 久99综合婷婷| 看日韩黄色片| 亚洲无码aaa| 99大香蕉| 少妇高潮毛片免费看欧美| 亚洲无码免费| 日韩无码免费| 欧美交换国产一区内射| 国产精品激情偷乱一区二区∴ | 韩国无码在线观看| 亚洲无码影院| 黑人无码| 国产无码在线看| 日本护士高潮乱喷www| 国产香蕉视频在线观看| 久久久精品电影| 国产精品白浆一区二小说| 国产精品欧美久久久久一区二区| 无码电影院| 99人妻碰碰碰久久久久禁片| 欧美色吧综合在线| 亚洲综合色网| 国产天天操| 99久久久无码国产精品无卡| 天堂中文字幕在线| 国产主播在线观看| 日韩视频精品| 午夜DV内射一区二区| 色婷婷91| 在线播放高清无码| 亚洲天天干| 91福利网| 一本一本久久a久久精品综合妖精| 国产精品久久久久久亚洲色欲| 欧美日韩精品一区二区在线播放| 在线观看a视频| 国产亚洲色婷婷久久99精品91| 亚洲综合成人网| 操逼国产| 综合成人网站| 日韩一二三四五区| 亚洲无码一区在线| 久久无码人妻丰满熟妇区毛片| 制服丝袜在线播放| 国产美女视频| 一级日韩| 96人伦影院A片在线观看| 人成视频在线免费观看| 日本大香蕉在线| 中文无码二区| 九九国产视频| 国产a级免费| 国产视频精品一区二区三区| 逼特逼视频在线观看| 欧洲精品无码一区二区三区在线| 欧美成人综合| 国产韩国日本欧美的品牌suv| 一级全黄60分钟免费网站| 久久亚洲区| 国产午夜精品一区| 中文字幕人妻一区二区| 最新国产在线| 在线视频中文字幕| 日本a视频| 国产在线观看AV| 中文字幕第四页| 麻豆网站在线观看| 美女喷水视频| 日韩三级在线观看| 少妇高潮视频| 精品人妻伦一二三区久久斗罗| 欧洲亚洲AV无码国产精品成人| 蜜桃久久| 中文字幕在线免费| 91AV视频在线观看| 青青草久久| 亚洲av无码一区二区三| 精品一区二区久久| 国产黄色片在线播放| 99视频精品全部在线观看下载| 毛片免费在线观看| 国产精品久久AV无码| 亚洲AV无码乱码| 91久久精品| 国产一级无码AV999毛片| 熟女VS乱伦| 亚洲色欲色| 成人免费无遮挡无码黄漫视频 | 亚洲中文国产精品| 夜夜躁狠狠躁日日躁麻豆老人| 日韩免费在线视频| 丰满岳乱妇一区二区三区| 草榴在线视频| 日本不卡在线| 欧美亚洲性爱| 国产午夜精品无码一区二区| 99热精品在线| 波多野结衣中文字幕久久| 欧韩在线视频| 国产青青操| 思思99热| 精品久久久久中文慕人妻| 99热国产在线观看| 在线观看国产黄片| 99久久免费看精品国产一区| 一级丰满老熟女毛片免费观看| 亚洲视频在线免费观看| 国产精品一区二区无码观看秘书| 日本福利片| 99热无码| 国产精品久久久久久婷婷天堂| 无码aⅴ精品日本无码久久| 亚洲精品字幕在线观看| 91久久久久久久久久久久| 另类小说综合网| 欧美第一页| 国产福利小视频在线观看| 亚州国产成人精品女人久久久| 亚洲天堂手机版| 人人操人人早| 国产乱子| 日日夜夜爽| 精品亚洲AV无码| 国产精品电影一区| 亚洲一区二区观看播放| 亚洲中文字幕无码AV| 久久午夜夜伦鲁鲁片无码免费| 亚洲av网站| 18禁网站在线| 亚洲欧洲无码AAA片在线观看| 国产爆乳成91人在线播放| 性生交大片免费全黄| 97视频在线免费观看| 色诱久久| 92久久精品一区二区| 殴美A片骚刺激爽| 亚洲综合小说| aV男人的天堂在线| 亚洲AV鲁丝一区二区三区 | 国产特级毛片AAAAAA| 久久国产精品精品国产色综合| 中文字幕人妻一区二区| 一级特黄60分钟高清免费观看| 欧洲多毛裸体xxxxx| 免费看一级黄片| 国产国产伦女伦一区二区三区| 精品无码在线| 啪啪免费| 久久久日韩精品无码一区二区| 国产精品无码一区二区三区绿巨人| 国产9999| 天堂AV一区| 狠狠操天天日| japan极品人妻videos| 国产成人小视频| 日韩乱码一区二区| 嫩草九九九精品乱码一二三| 亚洲一区自拍| 中文字幕人妻无码| 久久午夜无码鲁丝片午夜精品| 夜夜久久| 亚洲天堂无码| 成人免费毛片| 国产AV一区二区三区| 中文在线一区二区三区| 一级a一级a爰片免费免水l软件| 亚洲久草| 激情综合网欧美| 大香蕉久久久| 亚洲精品一级| 天天摸夜夜操| 99re6在线视频| 在线视频中文字幕| 无码人妻久久一区二区三区免费人妻| 欧美操逼视频| 久热国产精品| 欧美精品福利视频| 91视频免费观看| 日韩人妻视频| 亚洲电影在线观看| 欧美日韩免费| 国内精品久久久久久影视8| 女女同性女同区二区国产| 国产永久精品大片wwwApp| 亚洲人成色777777网站| 久久99亚洲精品久久99果冻| 五月天伊人| 中文在线中文资源| 在线观看无码AV| 国产男女无套免费视频| 国产女主播视频| 精品久久久久高清无码| 成人精品一区二区| 国产av一区二区三区四区| 91久久精品| 性欧美一区二区三区| 乱伦一区二区三区| 熟女肥臀白浆大屁股一区二区| 久久99精品久久久久久国产越南| 精品无码在线| 西西GOGO顶级艺术人像摄影| 成人乱人伦一区二区三区| 强开小婷嫩苞又嫩又紧视频| 国产福利视频在线观看| 久久人妻视频| 日韩黄片免费在线观看| 久久久一级片| 人妻体体内射精一区二区| 国产精品毛片一区视频播| 偷拍自拍AV| 免费无高潮片60分钟观看| a一级毛片| 日本乱伦精品| 国产日韩在线| 中日韩无码精品| 亚洲综合图片区| 精品福利在线| 成人网在线观看| 少妇熟女视频一区二区三区| 亚洲无码二区| 久久精品午夜| 日本一区二区不卡| 色香蕉av| 免费无码国产在线观看观喷水| 琪琪av| 色婷婷五月天在线观看| 黄色性爱网| 中文字幕在线一区二区三区| 五月天就要操| 擦逼视频国产| 免费日韩AV| 99久久久国产精品| 亚洲熟妇综合久久久久久| 黄片com| 精品乱子伦一区二区三区| 亚洲视频在线观看| 国产成人在线视频| 黄网在线观看| 国产精品无码一区二区三区| 国产a级免费| 69堂在线观看| 亚洲电影在线观看| 伊人毛片| 麻豆视频免费网站| 欧美一区久久| 欧美色综合一区二区三区| 乱伦天堂| 国产精品扒开腿做爽爽爽视频 | 国产成人小视频| 国产思思久久| 91色逼资源| 高清视频一区二区| 欧美日韩免费| 久久va| 国产看黄网站又黄又爽又色| 日韩无码天堂| 亚欧洲精品视频在线观看| 欧美拍拍| 在线不卡av| 少妇人妻真实偷人精品视频| 白浆内射| 97综合| 久久久久久久国产精品| 国产a一区| 大地资源网在线观看免费官网| jlzzjlzz国产精品久久| 中文字幕久久久| 精品国产乱码久久久久久果冻| 中文字幕乱码亚洲中文在线| 青青草91| 乱色熟女综合一区二区三区四| 亚洲欧洲一区| 综合网天天| 亚洲h片| 中文字幕免费看| 超碰香蕉| 69无码| 亚洲w欧洲无码sss222| 无码不卡在线| 国产精品99久久久久久白浆小说 | 人人看人人摸| 黄色18禁| 亚洲无码视频在线播放| 日韩精品一区二区三区在线观看视频网站| 久久久久久国产精品| 无码网站| 91久久偷偷做嫩草影院| 成人十区| 国产欧美一区二区三区在线看蜜臂| 国产中文字幕免费| 午夜视频网站在线观看| 亚洲AV激情无码专区在线播放| 欧美一区日韩一区| 无码中文av| 国产40-50熟女A片| 成人午夜sm精品久久久久久久| 日日夜夜精品| 另类TS人妖一区二区三区| 国产精品久久久久久久久久久久久四虎 | 一级毛片在线| 亚洲国产成人精品久久| 精品综合久久久| 99视频国产精品免费观看A| 亚洲熟女综合色一区二区三区| 中文字幕无码在线观看| 亚洲无码在线播放| 日韩免费无码| 超碰AV翔田千里| 521a人成v香蕉网站| 无码内射视频| 国产男生拳交女生在线播放| 欧美激情欧美激情在线五月| 日韩中文在线| 久久久久久精品免费看A级| 老熟妇乱伦一区二区| 色哟哟免费视频一区二区三区| 又大又粗又硬又爽又黄毛片视频| 日本特黄视频| 久久久久av| 日本高清老熟妇毛茸茸| 苍井空无码一区| 久久天天躁狠狠躁夜夜躁2014| 免费观看全黄做爰视频| 高清操逼无码| 91在线无码| 久久福利精品| 国产免费一区| 国产精品久久久久无码AV绿帽男| 影音先锋乱伦强奸| 久久精品视频一区| 日本久久99| 欧美亚洲三级| 国产一级片视频| 成人在线中文字幕| 国产香蕉97碰碰久久人人观看记录 | 色婷婷影院| 免费国产91| 天天操夜夜爽| 影音先锋男人av| 露脸对白| 香蕉AV777XXX色综合一区| 一区二区国产精品| 久久福利网| 国产一级片子| 国产精品播放| 岛国成人在线视频| 日韩日逼视频| 影音先锋中文字幕资源| 亚洲国产精品成人综合色在线婷婷| 久久久久逼| 日产成品片a直接观看| 无码不卡电影| 国产女人18水真多18精品一级做| 国产69精品久久久久APP下载| 欧美大片一区二区| 99精品欧美一区二区| 香蕉性爱视频| 成人高清| 欧美不卡一区二区| 青青操免费在线视频| 无码av中文| 91国偷自产一区二区三区老熟女| 毛片黄色| 免费在线看黄网站| 国产一二三视频| 91麻豆网| 色裕3区| 亚洲国产区| 日韩福利视频| 午夜av在线播放| 久久最新| 国产xxxxx| 91精品久久| 一级a一级a爰片免费免免在线| 欧美人人操人人摸| 精品一级毛片| 超碰99在线| brazzers欧美| 伊人网视频| 亚洲一级毛片| 全部孕妇孕交BBBBBB| 亚洲AV日韩AV永久无码网站| 韩国无码在线观看| 亚洲天堂成人网站| 人妻毛片| 日日夜夜精品| 偷拍二区| 久久成人精品| 欧美一区二区三区AA大片漫| 凹凸久久99精品久久久久久琪琪| 成人高清无码视频| 国产在线小电影| 一本一道波多野结衣一区二区| 韩国无码专区| 欧美成人精品一区二区男人看| 香蕉久久a毛片| 3P 内射 在线| 亚洲图片第一页| 精品无码人妻一区二区| 欧美九九| 久久精品国产亚洲AV麻豆图片| 东北浓毛老妇国语对白| 日韩激情网| 成人动漫在线观看| 久久福利网| 久久99精品久久久久久园产越南| 国产成人久久| 日韩人妻在线视频| 黄片在线免费观看| 国产精品一区二区三区四区| 亚洲欧洲视频| 91香蕉网| 久久无码区| 成人精品视频| 国产一级片视频| 日韩免费视频观看| 凹凸视频在线| 欧亚牲爱免费视频在线播放| 亚洲精品一区二区三区99| 99精品国产91久久久久久无码| va亚洲Va欧美va国产综合| 日韩在线观看网站| 91久久精品国产| 一本一道久久a久久精品综合色欲 亚洲一区二区免费在线观看 | 精品在线免费观看| 人妻精品久久无码专区一区二区| 熟女久久久| 试看日韩黄片|