浏览全部资源
扫码关注微信
Received：30 December 2020，
Revised：2021-1-13，
Accepted：20 January 2021，
Published：16 June 2021
移动端阅览
本文以视觉传感的新视觉传感硬件、处理技术和应用场景为主线，通过综合国内外文献和相关报道来梳理该领域在成像技术和数据处理方面的主要进展。从激光扫描成像、大动态范围光学成像技术、偏振成像与传感技术和海洋声学层析成像等研究方向，重点论述视觉传感领域的发展现状、前沿动态、热点问题和趋势。基于激光扫描的3维建模技术虽然取得了一些进展，但仍面临居多挑战。随着硬件设备和数据处理技术的发展，未来激光扫描系统将在众多民用领域得到广泛应用，满足不同的探测和建模任务；大动态范围光学成像相关技术已逐步应用于红外成像、光谱成像、偏振成像、超声成像和单光子成像等领域，将为多维信息获取、智能处理以及数据挖掘等提供有力支撑；充分挖掘偏振成像的应用潜能，与其他先进成像传感技术相结合，实现更优性能，对各个尺度下的成像场景都具有重要的应用价值；海洋声学层析成像需要与其他方法相结合，发展基于分布式水下传感网络、卫星观测、海底电缆、人工与自然噪声机会声源等联合观测的低成本、长期观测网络。对国内外视觉传感领域进展情况进行梳理、总结，有助于发现该领域的发展趋势以及明确下一步的研究方向。
Recently
significant developments of visual sensing have been observed in imaging technology and data processing
thereby providing great opportunities to enhance our ability to perceive and recognize our real world. Therefore
investigations on visual sensing possess important theoretical value and are required for application needs. Surveying the progress to understand the trend in the field of visual sensing and to clarify the future research direction is beneficial. The reviews are generated mainly based on analyzing peer-reviewed academic publications and related reports. A general description on the states of the art and trends about the visual sensing is provided
mainly including laser scanning
high dynamic range (HDR) imaging
polarization imaging
and ocean acoustic tomography. Specifically
for each of these imaging fields
parts discussed include new hardware
processing technology
and application scenarios. Processing of 3D point cloud data has become more effective along with the great progresses in deep learning and the advancement of hardware devices. Meanwhile
applications of 3D point cloud data are increasingly popular for diverse purposes. Over past several years
many domestic institutions and teams focused on developing algorithms for 3D point cloud data processing
such as in feature extraction
semantic labeling and segmentation
and object detection. In particular
several teams have conducted a number of substantive work in the production and sharing of standard data sets
which promote and improve the processing ability and application level of point cloud data. However
at present
the commercial hardware still has some deficiencies. Combining 3D point cloud data with observation from other sensors is a valuable but challenging task. Nevertheless
the laser scanning system is expected to be widely used in transportation
civil engineering
forestry
agriculture
and other civil fields in the future to satisfy different detection and modeling tasks. At the same time
with ongoing advancements in laser scanning equipment
it also plays an important role in understanding natural sciences
such as archaeology and geoscience. High dynamic range imaging is a hot research field in digital image acquisition
processing
display
and applications. Currently
researchers mostly focus on multiple exposure
different modulation methods
and multi detector methods in the HDR imaging. For example
through nonlinear response and multiple exposure imaging
the dynamic range can reach approximately 140 dB
and it can reach approximately 160 dB by using multi detector imaging. Using deep learning directly in HDR image mapping
instead of using traditional methodology
such as optical flow method and the combination of optical flow and neural network
has become a distinguished characteristic. Deep learning neural network has also been gradually applied to single exposure HDR reconstruction and tone mapping. Many domestic research teams have investigated the issues for the combination of deep learning neural network and HDR imaging. As expected
advancements in deep neural network provide a good opportunity for processing HDR imaging
such as in image fusion. With potential advancements in new detector materials
detector design
semiconductor equipment
and technology towards nanotechnology
new detectors with 10 megapixel resolution and dynamic range better than 160 dB will be available and will greatly improve the sensitivity under low illumination. Important fields urgently need breakthrough
including HDR imaging of dynamic scene and acquisition
processing and display of color HDR imaging with large dynamics
and wide color gamut. Compared with the progresses in polarization imaging made by other countries (e.g.
the United States of America
Canada
and Japan)
the systematicness and practicability in DoFP CMOS chip research domestically still need to be improved. In practice
the domestic institutions have made continuous achievements in many polarization imaging issues
including mosaic removal
polarization defogging
underwater polarization
polarization 3D imaging
imaging polarization spectral remote sensing
airborne polarization imaging
marine environment spectral polarization imaging
and spatial polarization detection. Furthermore
integrated optical detection of land
sea
air
and space is a critical demand
which promotes the rapid development of polarization imaging and sensing. In multisource data fusion
many methods and technologies showed excellent performance in their respective applications correspondingly
including multidimensional data acquisition and intelligent processing of "polarization +"
polarization + infrared
polarization + spectrum
polarization + TOF
polarization structured light
fluorescence polarization imaging (FPI)
polarization-sensitive optical coherence tomography (PS-OCT)
polarization-dependent optical second harmonic imaging
and polarization confocal microscopy imaging. In ocean acoustic tomography
institutions from the United States of America published the largest number of papers
showing distinguished trends from other countries. At the same time
as a country with the largest number of published patents
Japan shows great importance to ocean acoustic tomography and has certain advantages in technological innovation. Compared with institutions from the United States of America and Japan
institutions from China have published relatively a small number of papers and patents in ocean acoustic tomography. With more than 40 years of its development
great progresses were made in theory and technology. However
the application of ocean acoustic tomography still faces the bottleneck of high cost of sea trial
which is also impossibly used as an observation means alone. In conclusion
1) the 3D modeling based on laser scanning still faces many challenges
although progresses have been made recently. With the development of hardware and progress in data processing
laser scanning system benefits many civil fields in the future to satisfy different detection and modeling tasks; 2) high dynamic range optical imaging technology has been gradually applied to many fields
mainly including infrared imaging
spectral imaging
polarization imaging
ultrasonic imaging
and single photon imaging
which are valuable for multidimensional information acquisition
intelligent processing
and data mining; 3) fully exploiting the potential of polarization imaging has great value. Furthermore
to achieve its better performance
the combination with other advanced imaging sensing technologies is necessary. 4) Marine acoustic tomography needs to be combined with other means to develop a low-cost
long-term observation network
which is based on distributed underwater sensor networks
satellite observations
submarine cables
as well as using artificial and natural noise as sound source of opportunity.
Ahmed A, Zhao X J, Chang J T, Ma H, Gruev V and Bermak A. 2018. Four-directional adaptive residual interpolation technique for DoFP polarimeters with different micro-polarizer patterns. IEEE Sensors Journal, 18(19): 7990-7997[DOI:10.1109/JSEN.2018.2861825]
Banterle F, Artusi A, Sikudova E, Ledda P, Bashford-Rogers T, Chalmers A and Bloj M. 2016. Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments. Signal Processing: Image Communication, 48: 50-62[DOI:10.1016/j.image.2016.09.004]
Bao J C. 2015. HDR Image Tone Mapping Algorithms Based on Local Edge-Preserving Filtering. Xi'an: Xidian University
包江城. 2015. 基于局部边缘保持滤波的HDR图像色调映射算法. 西安: 西安电子科技大学
Chang M, Feng H J, Xu Z H and Li Q. 2018. Exposure correction and detail enhancement for single LDR image. Acta Photonica Sinica, 47(4): #0410003
常猛, 冯华君, 徐之海, 李奇. 2018. 单张LDR图像的曝光校正与细节增强. 光子学报, 47(4): #0410003)[DOI:10.3788/gzxb20184704.0410003]
Charles R Q, Su H, Kaichun M and Guibas L J. 2017. PointNet: deep learning on point sets for 3D classification and segmentation//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 77-85[ DOI: 10.1109/cvpr.2017.16 http://dx.doi.org/10.1109/cvpr.2017.16 ]
Chaudhari P, Schirrmacher F, Maier A, Riess C and Köhler T. 2019. Merging-ISP: multi-exposure high dynamic range image signal processing[EB/OL]. [2020-12-30] . https://arxiv.org/pdf/1911.04762.pdf https://arxiv.org/pdf/1911.04762.pdf
Chen D S, Zeng N, Xie Q L, He H H, Tuchin V V and Ma H. 2017a. Mueller matrix polarimetry for characterizing microstructural variation of nude mouse skin during tissue optical clearing. Biomedical Optics Express, 8(8): 3559-3570[DOI:10.1364/BOE.8.003559]
Chen H Z, Wang Y J, Sun H H, Chen C N and Fan B. 2013. High dynamic range imaging detection based on DMD and image sensor. Infrared and Laser Engineering, 42(12): 3402-3409
陈怀章, 王延杰, 孙宏海, 陈春宁, 樊博. 2013. DMD结合图像传感器的高动态场景成像探测. 红外与激光工程, 42(12): 3402-3409)[DOI:10.3969/j.issn.1007-2276.2013.12.044]
Chen Q B. 2014. Research on High Dynamic Range Image Display Technology. Wuhan: Huazhong University of Science and Technology
陈权斌. 2014. 高动态范围图像显示技术研究. 武汉: 华中科技大学
Chen X Z, Kundu K, Zhu Y K, Berneshawi A, Ma H M, Fidler S and Urtasun R. 2015a. 3D object proposals for accurate object class detection//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press: 424-432
Chen X Z, Ma H M, Wan J, Li B and Xia T. 2017b. Multi-view 3D object detection network for autonomous driving//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 6526-6534[ DOI: 10.1109/cvpr.2017.691 http://dx.doi.org/10.1109/cvpr.2017.691 ]
Chen Z G. 2018. Multiple Exposure Fusion Algorithm Parallel Optimization Based on OpenCL. Xi'an: Xidian University
陈志国. 2018. 基于OpenCL的多曝光融合算法并行优化. 西安: 西安电子科技大学
Chen Z Y, Wang X and Liang R G. 2015b. Snapshot phase shift fringe projection 3D surface measurement. Optics Express, 23(2): 667-673[DOI:10.1364/OE.23.000667]
Cheng H. 2019. Research on Tone Mapping Algorithm for High Dynamic Range Images. Chengdu: University of Chinese Academy of Sciences(Institute of Optics and Electronics, Chinese Academy of Sciences
程虹. 2019. 高动态范围图像的色调映射算法研究. 成都: 中国科学院大学(中国科学院光电技术研究所)
Choi S, Cho J, Song W, Choe J, Yoo J and Sohn K. 2020. Pyramid inter-attention for high dynamic range imaging. Sensors, 20(18): #5102[DOI:10.3390/s20185102]
DeFerrari H A and Nguyen H B. 1986. Acoustic reciprocal transmission experiments, Florida Straits. The Journal of the Acoustical Society of America, 79(2): 299-315[DOI:10.1121/1.393569]
Dong Z, Liang F X, Yang B S, Xu Y S, Zang Y F, Li J P, Wang Y, Dai W X, Fan H C, Hyyppä J and Stilla U. 2020. Registration of large-scale terrestrial laser scanner Point Clouds: a review and benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 327-342[DOI:10.1016/j.isprsjprs.2020.03.013]
Du J L, Chen D, Zhang Z X and Zhang L Q. 2019. Research progress of building reconstruction via airborne point clouds. Journal of Remote Sensing, 23(3): 374-391
杜建丽, 陈动, 张振鑫, 张立强. 2019. 建筑点云几何模型重建方法研究进展. 遥感学报, 23(3): 374-391)[DOI:10.11834/jrs.20188199]
Dushaw B D and the ATOC Group. 1999. The Acoustic Thermometry of Ocean Climate(ATOC) Project: towards depth-averaged temperature maps of the North Pacific Ocean//The International Symposium on Acoustic Tomography and Thermometry. Tokyo, Japan: [s. n.]
Dushaw B D, Worcester P F, Dzieciuch M A and Menemenlis D. 2013. On the time-mean state of ocean models and the properties of long range acoustic propagation. Journal of Geophysical Research: Oceans, 118(9): 4346-4362[DOI:10.1002/jgrc.20325]
Dushaw B D, Worcester P F, Munk W H, Spindel R C, Mercer J A, Howe B M, Metzger Jr. K, Birdsall T G, Andrew R K, Dzieciuch M A, Cornuelle B D and Menemenlis D. 2009. A decade of acoustic thermometry in the North Pacific Ocean. Journal of Geophysical Research: Oceans, 114(C7): #C07021[DOI:10.1029/2008 JC005124]
Dzieciuch M A. 2014. Signal processing and tracking of arrivals in ocean acoustic tomography. The Journal of the Acoustical Society of America, 136(5): 2512-2522[DOI:10.1121/1.4897404]
Eilertsen G, Kronander J, Denes G, Mantiuk R K and Unger J. 2017. HDR image reconstruction from a single exposure using deep CNNs. ACM Transactions on Graphics, 36(6): #178[DOI:10.1145/3130800.3130816]
Engelcke M, Rao D, Wang D Z, Tong C H and Posner I. 2017. Vote3Deep: fast object detection in 3D point clouds using efficient convolutional neural networks//Proceedings of 2017 IEEE International Conference on Robotics and Automation (ICRA). Singapore, Singapore: IEEE: 1355-1361[ DOI: 10.1109/icra.2017.7989161 http://dx.doi.org/10.1109/icra.2017.7989161 ]
Fenty I and Heimbach P. 2013. Coupled sea ice-ocean-state estimation in the Labrador Sea and Baffin Bay. Journal of Physical Oceanography, 43(5): 884-904[DOI:10.1175/JPO-D-12-065.1]
Gopalakrishnan G, Hoteit I, Cornuelle B D and Rudnick D L. 2019. Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico. Quarterly Journal of the Royal Meteorological Society, 145(721): 1354-1376[DOI:10.1002/qj.3493]
Granados M, Kim K I, Tompkin J and Theobalt C. 2013. Automatic noise modeling for ghost-free HDR reconstruction. ACM Transactions on Graphics, 32(6): 201[DOI:10.1145/2508363.2508410]
Gruev V. 2011. Fabrication of a dual-layer aluminum nanowires polarization filter array. Optics Express, 19(24): 24361-24369[DOI:10.1364/OE.19.024361]
Gruev V, Perkins R and York T. 2010. CCD polarization imaging sensor with aluminum nanowire optical filters. Optics Express, 18(18): 19087-19094[DOI:10.1364/OE.18.019087]
Guo Z and Feng C C. 2020. Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds. International Journal of Geographical Information Science, 34(4): 661-680[DOI:10.1080/13658816.2018.1552790]
Guthier B, Kopf S and Effelsberg W. 2012. A real-time system for capturing HDR videos//Proceedings of the 20th ACM international conference on Multimedia. Nara, Japan: Association for Computing Machinery: 1473-1476[ DOI: 10.1145/2393347.2396524 http://dx.doi.org/10.1145/2393347.2396524 ]
He L, Li Z L, Peng Z H, Wu L X and Liu J J. 2011. Acoustic inversion of sound speed profile in northern South China Sea. Scientia Sinica: Physica, Mechanica and Astronomica, 41(1): 49-57
何利, 李整林, 彭朝晖, 吴立新, 刘建军. 2011. 南海北部海水声速剖面声学反演. 中国科学: 物理学力学天文学, 41(1): 49-57)[DOI:10.1360/132010-907]
He L, Li Z L, Zhang R H and Li F H. 2006. Empirical orthogonal functions representation and matched field inversion of sound speed profile in East China Sea. Progress in Natural Science, 16(3): 351-355
何利, 李整林, 张仁和, 李风华. 2006. 东中国海声速剖面的经验正交函数表示与匹配场反演. 自然科学进展, 16(3): 351-355)[DOI:10.3321/j.issn:1002-008X.2006.03.015]
He S W, Wang Y J, Sun H H, Zhang L and Wu P. 2015. High dynamic range imaging based on DMD. Acta Photonica Sinica, 44(8): #0811001
何舒文, 王延杰, 孙宏海, 张雷, 吴培. 2015. 基于DMD的高动态范围场景成像技术. 光子学报, 44(8): #0811001)[DOI:10.3788/gzxb20154408.0811001]
He Y T. 2017. High Dynamic Range Image Synthesis Based on Image Fusion. Nanjing: Nanjing University
何玉婷. 2017. 基于图像融合的高动态范围图像合成. 南京: 南京大学
Howe B M. 1987. Multiple receivers in single vertical slice ocean acoustic tomography experiments. Journal of Geophysical Research: Oceans, 92(C9): 9479-9486[DOI:10.1029/JC092iC09p09479]
Howe B M, Dushaw B D, Mercer J A, Worcester P F, Colosi J A, Cornuelle B C, Dzieciuch M A and Spindel R C. 2003. Acoustic thermometry time series in the North Pacific//Proceedings of 2003 International Conference Physics and Control. Tokyo, Japan: IEEE: 111-114[ DOI: 10.1109/SSC.2003.1224123 http://dx.doi.org/10.1109/SSC.2003.1224123 ]
Hu Y X and Wan L. 2014. Multi exposure image fusion based on dynamic range extending. Computer Engineering and Application, 50(1): 153-155, 214
胡燕翔, 万莉. 2014. 大动态范围多曝光图像融合方法. 计算机工程与应用, 50(1): 153-155, 214)[DOI:10.3778/j.issn.1002-8331.1203-0003]
Huang H C, Guo Y, Wang Z K, Shen Y and Wei Y. 2019. Water temperature observation by coastal acoustic tomography in artificial upwelling area. Sensors, 19(12): #2655[DOI:10.3390/s19122655]
Huang R, Hong D F, Xu Y S, Yao W and Stilla U. 2020. Multi-scale local context embedding for LiDAR point cloud classification. IEEE Geoscience and Remote Sensing Letters, 17(4): 721-725[DOI:10.1109/LGRS.2019.2927779]
Huang S J. 2015. Research on the Technology of Geosynchronous Orbit High Dynamic Range Information Acquisition. Shanghai: University of Chinese Academy of Sciences (Shanghai Institute of Technical Physics of the Chinese Academy of Sciences
黄思婕. 2015. 地球静止轨道大动态范围信息获取技术研究. 上海: 中国科学院大学(中国科学院上海技术物理研究所)
Huang Y. 2017. Least Square Innovation Method for Ocean Acoustic Tomography. Hangzhou: Zhejiang University
黄颖. 2017. 深海声层析最小二乘新息方法. 杭州: 浙江大学
Ji X Y and Zhao H F. 2019. Travel-time correction and preliminary results for ocean acoustic tomography in South China Sea//Proceedings of the 2nd Franco-Chinese Acoustic Conference 2018. Le Mans, France: Curran Associates, Inc. : #04003[ DOI: 10.1051/matecconf/201928304003 http://dx.doi.org/10.1051/matecconf/201928304003 ]
Jiang D B. 2014. Study on Key Preprocessing Algorithms of Vision Sensing for Multiple Application Scenarios. Nanjing: Nanjing University
江登表. 2014. 多应用场景的视觉传感预处理关键算法研究. 南京: 南京大学
Jin G L, Lynch J F, Pawlowicz R and Wadhams P. 1993. Effects of sea ice cover on acoustic ray travel times, with applications to the Greenland Sea tomography experiment. The Journal of the Acoustical Society of America, 94(2): 1044-1057[DOI:10.1121/1.406951]
Kadambi A, Taamazyan V, Shi B X and Raskar R. 2017. Depth sensing using geometrically constrained polarization normals. International Journal of Computer Vision, 125(1/3): 34-51[DOI:10.1007/s11263-017-1025-7]
Kalantari N K and Ramamoorthi R. 2017. Deep high dynamic range imaging of dynamic scenes. ACM Transactions on Graphics, 36(4): 144[DOI:10.1145/3072959.3073609]
Khan Z, Khanna M and Raman S. 2019. FHDR: HDR image reconstruction from a single LDR image using feedback network//Proceedings of 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Ottawa, Canada: IEEE: 1-5[ DOI: 10.1109/GlobalSIP45357.2019.8969167 http://dx.doi.org/10.1109/GlobalSIP45357.2019.8969167 ]
Kim B K, Park R H and Chang S. 2016. Tone mapping with contrast preservation and lightness correction in high dynamic range imaging. Signal, Image and Video Processing, 10(8): 1425-1432[DOI:10.1007/s11760-016-0942-1]
Kim D, Jung K, Ham B, Kim Y and Sohn K. 2014. Normalized tone-mapping operators for color quality improvement in 3DTV//Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications. Hangzhou, China: IEEE: 430-435[ DOI: 10.1109/iciea.2014.6931201 http://dx.doi.org/10.1109/iciea.2014.6931201 ]
Köstler H, Stürmer M and Pohl T. 2016. Performance engineering to achieve real-time high dynamic range imaging. Journal of Real-Time Image Processing, 11(1): 127-139[DOI:10.1007/s11554-012-0312-3]
Kulkarni M and Gruev V. 2012. Integrated spectral-polarization imaging sensor with aluminum nanowire polarization filters. Optics Express, 20(21): 22997-23012[DOI:10.1364/OE.20.022997]
Landrieu L and Simonovsky M. 2018. Large-scale point cloud semantic segmentation with superpoint graphs//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 4558-4567[ DOI: 10.1109/cvpr.2018.00479 http://dx.doi.org/10.1109/cvpr.2018.00479 ]
Lang A H, Vora S, Caesar H, Zhou L B, Yang J and Beijbom O. 2019. PointPillars: fast encoders for object detection from point clouds//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 12689-12697[ DOI: 10.1109/cvpr.2019.01298 http://dx.doi.org/10.1109/cvpr.2019.01298 ]
Lapray P J, Heyrman B and Ginhac D. 2015. High dynamic range adaptive real-time smart camera: an overview of the HDR-ARTiST project//Proceedings Volume 9534, 12th International Conference on Quality Control by Artificial Vision 2015. Le Creusot, France: SPIE: 953417[ DOI: 10.1117/12.2182844 http://dx.doi.org/10.1117/12.2182844 ]
Lebedev K V, Yaremchuk M, Mitsudera H, Nakano I and Yuan G. 2003. Monitoring the Kuroshio extension with dynamically constrained synthesis of the acoustic tomography, satellite altimeter and in situ data. Journal of Oceanography, 59(6): 751-763[DOI:10.1023/B:JOCE.0000009568.06949.c5]
Lee C. 2019. Combining advanced image signal processing with accelerated edge inference for automotive viewing and sensing camera//Proceedings of 2019 IEEE International Conference on Image Processing(ICIP). Taipei, China: IEEE: 2458[ DOI: 10.1109/ICIP.2019.8803327 http://dx.doi.org/10.1109/ICIP.2019.8803327 ]
Leng B, Guo S, Zhang X Y and Xiong Z. 2015. 3D object retrieval with stacked local convolutional autoencoder. Signal Processing, 112: 119-128[DOI:10.1016/j.sigpro.2014.09.005]
Li B. 2017. 3D fully convolutional network for vehicle detection in point cloud//Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver BC, Canada: IEEE: 1513-1518[ DOI: 10.1109/iros.2017.8205955 http://dx.doi.org/10.1109/iros.2017.8205955 ]
Li B, Zhang T L and Xia T. 2016. Vehicle detection from 3D Lidar using fully convolutional network[EB/OL]. [2020-10-05] . https://arxiv.org/pdf/1608.07916.pdf https://arxiv.org/pdf/1608.07916.pdf
Li C, Wang P and Bi D Y. 2018. Realistic image rendition based on intersecting cortical model. Journal of University of Electronic Science and Technology of China, 47(2): 272-278
李成, 汪沛, 毕笃彦. 2018. 基于交叉视觉皮质模型的真实图像再现方法. 电子科技大学学报, 47(2): 272-278)[DOI:10.3969/j.issn.1001-0548.2018.02.018]
Li J H and Fang P Y. 2019. HDRNet: single-image-based HDR reconstruction using channel attention CNN//Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing. Guangzhou, China: Association for Computing Machinery: 119-124[ DOI: 10.1145/3330393.3330426 http://dx.doi.org/10.1145/3330393.3330426 ]
Li J L, Xu W, Jin L L and Guo S M. 2012. Study of acoustic tomography based on data assimilation for shallow water environments. Acta Acustica, 37(1): 10-17
李建龙, 徐文, 金丽玲, 郭圣明. 2012. 浅海环境下数据同化声层析方法研究. 声学学报, 37(1): 10-17)[DOI:10.15949/j.cnki.0371-0025.2012.01.002]
Li L X, Li Y Q, Wang L, Niu L B, Huang T D and Li Y P. 2017. Fine segmentation of building facade combined mobile LiDAR with airborne LiDAR point cloud data. Journal of Geomatics Science and Technology, 34(2): 181-186
李立雪, 李永强, 王力, 牛路标, 黄腾达, 李有鹏. 2017. 车载联合机载LiDAR点云数据的建筑物立面精细分割. 测绘科学技术学报, 34(2): 181-186)[DOI:10.3969/j.issn.1673-6338.2017.02.013]
Li X A. 2018. Research and Implementation of Multi-Exposure Image Fusion Based on Convolutional Neural Networks. Jinan: Shandong University
李雪奥. 2018. 基于卷积神经网络的多曝光图像融合方法研究与实现. 济南: 山东大学
Li Y Y. 2016. Image Dynamic Range Broadening Method Research. Xi'an: Chang'an University
李莹莹. 2016. 图像动态范围展宽方法研究. 西安: 长安大学
Li Y Y, Bu R, Sun M C, Wu W, Di X H and Chen B Q. 2018b. PointCNN: convolution on χ -transformed points//Proceedings of the 32nd Conference on Neural Information Processing Systems. Montréal, Canada: Curran Associates Inc. : 820-830
Lin Y B, Wang C, Zhai D W, Li W and Li J. 2018. Toward better boundary preserved supervoxel segmentation for 3D point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 143: 39-47[DOI:10.1016/j.isprsjprs.2018.05.004]
Liu F, Han P L, Wei Y, Yang K, Huang S Z, Li X, Zhang G, Bai L and Shao X P. 2018. Deeply seeing through highly turbid water by active polarization imaging. Optics Letters, 43(20): 4903-4906[DOI:10.1364/OL.43.004903]
Liu L L, Xiang X Q, Xie Y X, Li Y J and Zhou J. 2019. A high throughput and energy-efficient retina-inspired tone mapping processor//Proceedings of the 27th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines. San Diego, USA: IEEE: 321[ DOI: 10.1109/FCCM.2019.00062 http://dx.doi.org/10.1109/FCCM.2019.00062 ]
Liu Y, Lv B X, Huang W, Jin B H and Li C L. 2020. Anti-shake HDR imaging using RAW image data. Information, 11(4): 213[DOI:10.3390/info11040213]
Liu Z Y. 2016. The Research on High Dynamic Range Image Synthesizing and Tone Mapping. Harbin: Harbin Engineering University
刘宗玥. 2016. 高动态范围图像的合成与色阶映射的研究. 哈尔滨: 哈尔滨工程大学
Lu X M, Zhu X Y and Li Z W. 2015. A brightness-scaling and detail-preserving tone mapping method for high dynamic range images. Acta Automatica Sinica, 41(6): 1080-1092
陆许明, 朱雄泳, 李智文. 2015. 一种亮度可控与细节保持的高动态范围图像色调映射方法. 自动化学报, 41(6): 1080-1092)[DOI:10.16383/j.aas.2015.c130202]
Manakov A, Restrepo J F, Klehm O, Hegedüs R, Eisemann E, SeidelH P and Ihrke I. 2013. A reconfigurable camera add-on for high dynamic range, multispectral, polarization, and light-field imaging. ACM Transactions on Graphics, 32(4): #47[DOI:10.1145/2461912.2461937]
Marnerides D, Bashford-Rogers T, Hatchett J and Debattista K. 2018. ExpandNet: a deep convolutional neural network for high dynamic range expansion from low dynamic range content. Computer Graphics Forum, 37(2): 37-49[DOI:10.1111/cgf.13340]
Maturana D and Scherer S. 2015a. VoxNet: a 3D convolutional neural network for real-time object recognition//Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany: IEEE: 922-928[ DOI: 10.1109/iros.2015.7353481 http://dx.doi.org/10.1109/iros.2015.7353481 ]
Maturana D and Scherer S. 2015b. 3D convolutional neural networks for landing zone detection from LiDAR//Proceedings of 2015 IEEE International Conference on Robotics and Automation. Seattle, USA: IEEE: 3471-3478[ DOI: 10.1109/icra.2015.7139679 http://dx.doi.org/10.1109/icra.2015.7139679 ]
Meier W N, Hovelsrud G K, van Oort B E, Key J R, Kovacs K M, Michel C, Haas C, Granskog M A, Gerland S, Perovich D K, Makshtas A and Reist J D. 2014. Arctic sea ice in transformation: a review of recent observed changes and impacts on biology and human activity. Reviews of Geophysics, 52(3): 185-217[DOI:10.1002/2013RG000431]
Merianos I and Mitianoudis N. 2016. A hybrid multiple exposure image fusion approach for HDR image synthesis//Proceedings of 2016 IEEE International Conference on Imaging Systems and Techniques. Chania, Greece: IEEE: 222-226[ DOI: 10.1109/IST.2016.7738227 http://dx.doi.org/10.1109/IST.2016.7738227 ]
Metzler C A, Ikoma H, Peng Y F and Wetzstein G. 2019. Deep optics for single-shot high-dynamic-range imaging[EB/OL]. [2020-11-20] . https://arxiv.org/pdf/1908.00620.pdf https://arxiv.org/pdf/1908.00620.pdf
Mikhalevsky P N and Gavrilov A N. 2001. Acoustic thermometry in the Arctic Ocean. Polar Research, 20(2): 185-192[DOI:10.1111/j.1751-8369.2001.tb00055.x]
Mody M, Nandan N, Allu H S R and Sagar R. 2015. Flexible wide dynamic range (WDR) processing support in image signal processor (ISP)//Proceedings of 2015 IEEE International Conference on Consumer Electronics. Las Vegas, USA: IEEE: 467-470[ DOI: 10.1109/ICCE.2015.7066488 http://dx.doi.org/10.1109/ICCE.2015.7066488 ]
Moriwaki K, Yoshihashi R, Kawakami R, You S D and Naemura T. 2018. Hybrid loss for learning single-image-based HDR reconstruction[EB/OL]. [2020-12-30] . https://arxiv.org/pdf/1812.07134.pdf https://arxiv.org/pdf/1812.07134.pdf
Mu T K, Pacheco S, Chen Z Y, Zhang C M and Liang R G. 2017. Snapshot linear-Stokes imaging spectropolarimeter using division-of-focal-plane polarimetry and integral field spectroscopy. Scientific Reports, 7: #42115[DOI:10.1038/srep42115]
Munk W H, Worcester P andWunsch C. 1995. Ocean Acoustic Tomography. Cambridge: Cambridge University Press
Nguyen A T, Ocaña V, Garg V, Heimbach P, Toole J M, Krishfield R A, Lee C M and Rainville L. 2017. On the benefit of current and future ALPS data for improving Arctic coupled ocean-sea ice state estimation. Oceanography, 30(2): 69-73[DOI:10.5670/oceanog.2017.223]
Ou Y F, Ambalathankandy P, Ikebe M, Takamaeda S, Motomura M and Asai T. 2020. Real-time tone mapping: a state of the art report[EB/OL]. [2020-10-09] https://arxiv.org/pdf/2003.03074.pdf https://arxiv.org/pdf/2003.03074.pdf
Pan J S, Gu Y, Li Y H, Sun J N, Zhang Q D and Su D T. 2017. Large dynamic range science intensified camera with single photon sensitivity and high spatiotemporal resolution. Infrared Technology, 39(9): 864-870
潘京生, 顾燕, 李燕红, 孙建宁, 张勤东, 苏德坦. 2017. 单光子灵敏和高时空分辨的大动态范围科学增强相机. 红外技术, 39(9): 864-870
Papon J, Abramov A, Schoeler M and Wörgötter F. 2013. Voxel cloud connectivity segmentation-supervoxels for point clouds//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE: 2027-2034[ DOI: 10.1109/cvpr.2013.264 http://dx.doi.org/10.1109/cvpr.2013.264 ]
Park, J W, Lee H, Kim B, Kang D G, Jin S O, Kim H and Lee H J. 2020. A low-cost and high-throughput FPGA implementation of the retinex algorithm for real-time video enhancement. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28(1): 101-114[DOI:10.1109/TVLSI.2019.2936260]
Pu Y L. 2015. Research and Implementation of High Dynamic Range Image Tone Mapping. Wuhan: Huazhong University of Science and Technology
蒲雅蕾. 2015. 高动态范围图像色调映射方法的研究与实现. 武汉: 华中科技大学
Qi C R, Litany O, He K M and Guibas L. 2019. Deep hough voting for 3D object detection in point clouds//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 9276-9285[ DOI: 10.1109/iccv.2019.00937 http://dx.doi.org/10.1109/iccv.2019.00937 ]
Qi C R, Su H, Nieβner M, Dai A, Yan M Y and Guibas L J. 2016. Volumetric and multi-view CNNs for object classification on 3D data//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 5648-5656[ DOI: 10.1109/cvpr.2016.609 http://dx.doi.org/10.1109/cvpr.2016.609 ]
Qi C R, Yi L, Su H and Guibas L J. 2017. Pointnet++: deep hierarchical feature learning on point sets in a metric space//Proceedings of the 31 st Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates, Inc. : 5099-5108
Qiu Y. 2019. Deep Ocean Acoustic Tomography with Mobile Node. Hangzhou: Zhejiang University
邱炎. 2019. 深海移动声层析研究. 杭州: 浙江大学
Rana A, Singh P, Valenzise G, Dufaux F, Komodakis N and Smolic A. 2020. Deep tone mapping operator for high dynamic range images. IEEE Transactions on Image Processing, 29: 1285-1298[DOI:10.1109/TIP.2019.2936649]
Riza N A and Mazhar M A. 2020. CAOS smart camera-based robust low contrast image recovery over 90 dB scene linear dynamic range. Electronic Imaging, Imaging Sensors and Systems, 2020(7): #226[DOI:10.2352/ISSN.2470-1173.2020.7.ISS-226].
Santos M S, Ren T I and Kalantari N K. 2020. Single image HDR reconstruction using a CNN with masked features and perceptual loss. ACM Transactions on Graphics, 39(4): 80[DOI:10.1145/3386569.3392403]
Sen P, Kalantari N K, Yaesoubi M, Darabi S and Goldman D B. 2012. Robust patch-based HDR reconstruction of dynamic scenes. ACM Transactions on Graphics, 31(6): 203[DOI:10.1145/2366145.2366222]
Seshadrinathan K and Nestares O. 2017. High dynamic range imaging using camera arrays//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 725-729[ DOI: 10.1109/ICIP.2017.8296376 http://dx.doi.org/10.1109/ICIP.2017.8296376 ]
Shen T S. 2018. Maximum Entropy Particle Filter for Ocean Acoustic Tomography. Hangzhou: Zhejiang University
申屠帅. 2018. 海洋声层析最大熵粒子滤波方法. 杭州: 浙江大学
Shi S S, Wang X G and Li H S. 2019. PointRCNN: 3D object proposal generation and detection from point cloud//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 770-779[ DOI: 10.1109/cvpr.2019.00086 http://dx.doi.org/10.1109/cvpr.2019.00086 ]
Smith W A P, Ramamoorthi R and Tozza S. 2019. Height-from-polarisation with unknown lighting or albedo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12): 2875-2888[DOI:10.1109/TPAMI.2018.2868065]
Stammer D, Balmaseda M, Heimbach P, Köhl A and Weaver A. 2016. Ocean data assimilation in support of climate applications: status and perspectives. Annual Review of Marine Science, 8: 491-518[DOI:10.1146/annurev-marine-122414-034113]
Su H, Maji S, Kalogerakis E and Learned-Miller E. 2015. Multi-view convolutional neural networks for 3D shape recognition//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 945-953[ DOI: 10.1109/iccv.2015.114 http://dx.doi.org/10.1109/iccv.2015.114 ]
Sun C K. 2015. Research and implementation of high dynamic range imagemapping algorithm based on bilateral filtering. Xi'an: Xidian University
孙晨康. 2015. 基于双边滤波的高动态范围图像映射算法研究与实现. 西安: 西安电子科技大学
Sun H H, He S W, Wu P and Wang Y J. 2017. Design and imaging analysis of high dynamics scientific CMOS camera. Chinese Journal of Liquid Crystals and Displays, 32(3): 240-248
孙宏海, 何舒文, 吴培, 王延杰. 高动态科学级CMOS相机设计与成像分析. 液晶与显示, 32(3): 240-248)[DOI:10.3788/YJYXS20173203.0240]
Sun H H, Wang Y J, Yang H and Wu P. 2019. Binocular high dynamic range imaging system based on digital micromirror device. Optical and Quantum Electronics, 51(9): #307[DOI:10.1007/s11082-019-2014-6]
Sun Q L, Tseng E, Fu Q, Heidrich W and Heide F. 2020. Learning Rank-1 diffractive optics for single-shot high dynamic range imaging//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 1383-1393[ DOI: 10.1109/CVPR42600.2020.00146 http://dx.doi.org/10.1109/CVPR42600.2020.00146 ]
Sun W, Han C S, Jin X F, Lv H Y and Liu H Y. 2018. HDR imaging method of overcoming full well limitation for push-broom remote sensing cameras. Optics and Precision Engineering, 26(4): 944-950
孙武, 韩诚山, 晋学飞, 吕恒毅, 刘海龙. 2018. 推扫式遥感相机超满阱大动态范围成像. 光学精密工程, 26(4): 944-950)[DOI:10.3788/OPE.20182604.0944]
Tang J F and Yang S E. 2006. Sound speed profile in ocean inverted by using travel time. Journal of Harbin Engineering University, 27(5): 733-736, 756
唐俊峰, 杨士莪. 2006. 由传播时间反演海水中的声速剖面. 哈尔滨工程大学学报, 27(5): 733-736, 756)[DOI:10.3969/j.issn.1006-7043.2006.05.022]
Tang X D, Qian Y S, Kong X Y and Wang H G. 2020. A high-dynamic range CMOS camera based on dual-gain channels. Journal of Real-Time Image Processing, 17(3): 703-712[DOI:10.1007/s11554-019-00877-8]
Tozza S, Smith W A P, Zhu D Z, Ramamoorthi R and Hancock E R. 2017. Linear differential constraints for photo-polarimetric height estimation//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2298-2306[ DOI: 10.1109/ICCV.2017.250 http://dx.doi.org/10.1109/ICCV.2017.250 ]
Tu X Z, Spires O J, Tian X B, Brock N, Liang R G and Pau S. 2017. Division of amplitude RGB full-Stokes camera using micro-polarizer arrays. Optics Express, 25(26): 33160-33175[DOI:10.1364/OE.25.033160]
Vorobiev D V, Ninkov Z and Brock N. 2018. Astronomical polarimetry with the RIT polarization imaging camera. Publications of the Astronomical Society of the Pacific, 130: 064501[DOI:10.1088/1538-3873/aab99b]
Wang C, Wen C L, Dai Y S, Yu S S and Liu M H. 2020a. Urban 3D modeling with mobile laser scanning: a review. Virtual Reality and Intelligent Hardware, 2(3): 175-212[DOI:10.1016/j.vrih.2020.05.003]
Wang D Z and Posner I. 2015. Voting for voting in online point cloud object detection//Proceedings of the Robotics: Science and Systems. Rome, Italy: [s. n.]: #1317[ DOI: 10.15607/rss.2015.xi.035 http://dx.doi.org/10.15607/rss.2015.xi.035 ]
Wang F Y. 2016. Travel-Time Sensitivity Kernel Method for Ocean Acoustic Tomography. Hangzhou: Zhejiang University
汪非易. 2016. 深海声层析传播时延敏感核方法. 杭州: 浙江大学
Wang H Y, Wang C, Luo H, Li P, Chen Y P and Li J. 2015a. 3-D point cloud object detection based on supervoxel neighborhood with hough forest framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1570-1581[DOI:10.1109/jstars.2015.2394803]
Wang T C, Zhang Y, Yang T C, Chen H F and Xu W. 2018. Physics-based coastal current tomographic tracking using a Kalman filter. The Journal of the Acoustical Society of America, 143(5): 2938-2953[DOI:10.1121/1.5036755]
Wang X S, Chan T O, Liu K, Pan J, Luo M, Li W K and Wei C Z. 2020b. A robust segmentation framework for closely packed buildings from airborne LiDAR point clouds. International Journal of Remote Sensing, 41(14): 5147-5165[DOI:10.1080/01431161.2020.1727053]
Wang Y, Sun Y B, Liu Z W, Sarma S E, Bronstein M M and Solomon J M. 2019. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38(5): 146[DOI:10.1145/3326362]
Wang Z, Zhang L Q, Fang T, Mathiopoulos P T, Tong X H, Qu H M, Xiao Z Q, Li F and Chen D. 2015b. A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2409-2425[DOI:10.1109/tgrs.2014.2359951]
Worcester P F, Dzieciuch M A, Mercer J A, Andrew R K, Dushaw B D, Baggeroer A B, Heaney K D, D'Spain G L, Colosi J A, Stephen R A, Kemp J N, Howe B M, Van Uffelen L J and Wage K E. 2013. The north pacific acoustic laboratory deep-water acoustic propagation experiments in the Philippine Sea. The Journal of the Acoustical Society of America, 134(4): 3359-3375[DOI:10.1121/1.4818887]
Wu H Y, Li X K and Hu Y. 2007. Approach on interactive extraction of gable-roofed building models from airborne Lidar data. Journal of Image and Graphics, 12(3): 474-481
吴华意, 李新科, 胡勇. 2007. 从机载激光扫描数据中交互式提取人字形房屋模型的方法研究. 中国图象图形学报, 12(3): 474-481)[DOI:10.11834/jig.20070316]
Wu J H, Yu B L, Peng C, Wu B, Yu S Y, Huang Y X and Wu J P. 2016. A method for fast modeling of 3D buildings from Mobile Laser Scanning point clouds and remote sensing data. Geomatics and Spatial Information Technology, 39(1): 24-27, 34
吴君涵, 余柏蒗, 彭晨, 吴宾, 虞思逸, 黄益修, 吴健平. 2016. 基于移动激光扫描点云数据和遥感图像的建筑物三维模型快速建模方法. 测绘与空间地理信息, 39(1): 24-27, 34)[DOI:10.3969/j.issn.1672-5867.2016.01.008]
WuR. 2020. Research on Multi-Exposure HDR Imaging Algorithm Based on Convolutional Neural Network. Chengdu: University of Electronic Science and Technology
吴蕊. 2020. 基于卷积神经网络的多曝光HDR成像算法研究. 成都: 电子科技大学
Wu S Z, Xu J R, Tai Y W and Tang C K. 2018. Deep high dynamic range imaging with large foreground motions//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer: 120-135[ DOI: 10.1007/978-3-030-01216-8_8 http://dx.doi.org/10.1007/978-3-030-01216-8_8 ]
Wu Z R, Song S R, Khosla A, Yu F, Zhang L G, Tang X O and Xiao J X. 2015. 3D ShapeNets: a deep representation for volumetric shapes//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 1912-1920[ DOI: 10.1109/cvpr.2015.7298801 http://dx.doi.org/10.1109/cvpr.2015.7298801 ]
Wunsch C. 2020. Advance in global ocean acoustics. Science, 369(6510): 1433-1434[DOI:10.1126/science.abe0960]
Xiao J S, Li W H, Liu G X, Shaw S L and Zhang Y Q. 2014. Hierarchical tone mapping based on image colour appearance model. IET Computer Vision, 8(4): 358-364[DOI:10.1049/iet-cvi.2013.0230]
Xie S D, Wu W F, Chen R J and Tan H Z. 2020. Reduced-dimensional capture of high-dynamic range images with compressive sensing. Discrete Dynamics in Nature and Society, 2020: #6703528[DOI:10.1155/2020/6703528]
Xu B, Wang L Z and Duan T H. 2019a. A novel hybrid calibration method for FOG-based IMU. Measurement, 147: #106900[DOI:10.1016/j.measurement.2019.106900]
Xu M M and Hua H. 2017. High dynamic range head mounted display based on dual-layer spatial modulation. Optics Express, 25(19): 23320-23333[DOI:10.1364/OE.25.023320]
Xu Y C, Ning S Y, Xie R and Song L. 2019b. Gan based multi-exposure inverse tone mapping//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei, China: IEEE: 1-5[ DOI: 10.1109/ICIP.2019.8803540 http://dx.doi.org/10.1109/ICIP.2019.8803540 ]
Xu Y S, Ye Z, Yao W, Huang R, Tong X H, Hoegner L and Stilla U. 2020. Classification of LiDAR point clouds using supervoxel-based detrended feature and perception-weighted graphical model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 72-88[DOI:10.1109/jstars.2019.2951293]
Xue X C, Han C S, Xue D L and Guo Y F. 2012. Increasing dynamic range of space push-broom remote sensing camera by two-row TDI CCD. Optics and Precision Engineering, 20(12): 2791-2975
薛旭成, 韩诚山, 薛栋林, 郭永飞. 2012. 应用双排TDI CCD提高空间推扫遥感相机动态范围. 光学精密工程, 20(12): 2791-2975)[DOI:10.3788/OPE.20122012.2791]
Yamashita T and Fujita Y. 2017. HDR video capturing system with four image sensors. ITE Transactions on Media Technology and Applications, 5(4): 141-146[DOI:10.3169/mta.5.141]
Yan Q S, Gong D, Shi Q F, van den Hengel A, Shen C H, Reid I and Zhang Y N. 2019. Attention-guided network for ghost-free high dynamic range imaging//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 1751-1760[ DOI: 10.1109/cvpr.2019.00185 http://dx.doi.org/10.1109/cvpr.2019.00185 ]
Yang B, Luo W J and Urtasun R. 2018. PIXOR: real-time 3D object detection from point clouds//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7652-7660[ DOI: 10.1109/cvpr.2018.00798 http://dx.doi.org/10.1109/cvpr.2018.00798 ]
Yang J, Shahnovich U and Yadid-Pecht O. 2020. Mantissa-exponent-based tone mapping for wide dynamic range image sensors. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 67(1): 142-146[DOI:10.1109/TCSⅡ.2019.2903101]
Ye N J. 2020. Research on HDR Imaging Method Based on Deep Learning. Chengdu: University of Electronic Science and Technology
叶年进. 2020. 基于深度学习的HDR成像方法研究. 成都: 电子科技大学
Yu X J, Zhuang X B, Li Y L and Zhang Y. 2019. Real-time observation of range-averaged temperature by high-frequency underwater acoustic thermometry. IEEE Access, 7: 17975-17980[DOI:10.1109/ACCESS.2019.2894341]
Zai D W, Li J, Guo Y L, Cheng M, Lin Y B, Luo H and Wang C. 2018. 3-D road boundary extraction from mobile laser scanning data via supervoxels and graph cuts. IEEE Transactions on Intelligent Transportation Systems, 19(3): 802-813[DOI:10.1109/tits.2017.2701403]
Zhang D Y. 2015. Research on High Dynamic Range Imaging Based on FPGA. Guangzhou: South China University of Technology
张东阳. 2015. 基于FPGA的高动态范围成像技术研究. 广州: 华南理工大学
Zhang J and Singh S. 2015. Visual-lidar odometry and mapping: low-drift, robust, and fast//Proceedings of 2015 IEEE International Conference on Robotics and Automation. Seattle, USA: IEEE: 2174-2181[ DOI: 10.1109/icra.2015.7139486 http://dx.doi.org/10.1109/icra.2015.7139486 ]
Zhang J and Singh S. 2018. Laser-visual-inertial odometry and mapping with high robustness and low drift. Journal of Field Robotics, 35(8): 1242-1264[DOI:10.1002/rob.21809]
Zhang W, Yang S E and Huang Y. 2015. Research about sensitivity of array inclination on sound speed profile inversion. Acta Acustica, 40(5): 649-654
张维, 杨士莪, 黄勇. 2015. 声速剖面反演对基阵倾斜失配的敏感性研究. 声学学报, 40(5): 649-654)[DOI:10.15949/j.cnki.0371-0025.2015.05.005]
Zhang X S and Li Y J. 2016. A retina inspired model for high dynamic range image rendering//Proceedings of the 8th International Conference on Advances in Brain Inspired Cognitive Systems. Beijing, China: Springer: 68-79[ DOI: 10.1007/978-3-319-49685-6_7 http://dx.doi.org/10.1007/978-3-319-49685-6_7 ]
Zhang Z G, Dong F L, Cheng T, Qiu K, Zhang Q C, Chu W G and Wu X P. 2014. Nano-fabricated pixelated micropolarizer array for visible imaging polarimetry. Review of Scientific Instruments, 85(10): 105002[DOI:10.1063/1.4897270]
Zhao G W. 2019. The Design and Optimization of HDR Video Generation System Based on Heterogeneous Platforms. Xi'an: Xidian University
赵高伟. 2019. 基于异构平台的HDR视频生成系统的设计及优化. 西安: 西安电子科技大学
Zhao H F, Wang F Y, Zhu X H and Xu W. 2015. Ocean acoustic tomography: current progress and future prospect. Journal of Ocean Technology, 34(3): 69-74
赵航芳, 汪非易, 朱小华, 徐文. 2015. 海洋声学层析研究现状与展望. 海洋技术学报, 34(3): 69-74
Zhao X J, Boussaid F, Bermak A and Chigrinov V G. 2009. Thin photo-patterned micropolarizer array for CMOS image sensors. IEEE Photonics Technology Letters, 21(12): 805-807[DOI:10.1109/LPT.2009.2018472]
Zheng C, Bernal S G, Zhang K Q, Mao L and Liu X. 2019. A novel high dynamic range microscopic video system based on REC. 2020. Optical Instruments, 41(3): 35-41
郑驰, Bernal S G, 张克奇, 毛磊, 刘旭. 2019. 一种基于REC. 2020高清色域的视频级显微高动态范围成像方法. 光学仪器, 41(3): 35-41)[DOI:10.3969/j.issn.1005-5630.2019.03.006]
Zhou J, Qiao Y, Sun Z, Chen H and Chen H. 2019. Design of a dual DMDs camera for high dynamic range imaging. Optics Communications, 452: 140-145[DOI:10.1016/j.optcom.2019.07.008]
Zhou Y and Tuzel O. 2018. VoxelNet: end-to-end learning for point cloud based 3D object detection//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 4490-4499[ DOI: 10.1109/cvpr.2018.00472 http://dx.doi.org/10.1109/cvpr.2018.00472 ]
Zhu D Z and Smith W A P. 2019. Depth from a polarisation + RGB stereo pair//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 7578-7587[ DOI: 10.1109/CVPR.2019.00777 http://dx.doi.org/10.1109/CVPR.2019.00777 ]
Zhu X H, Kaneko A, Wu Q S, Gohda N, Zhang C Z and Taniguchi N. 2010. The first Chinese coastal acoustic tomography experiment//Proceedings of OCEANS'10 IEEE SYDNEY. Sydney, NSW, Australia: IEEE: 1-4[ DOI: 10.1109/OCEANSSYD.2010.5603798 http://dx.doi.org/10.1109/OCEANSSYD.2010.5603798 ]
Zhu X H, Kaneko A, Wu Q S, Zhang C Z, Taniguchi N and Gohda N. 2013. Mapping tidal current structures in Zhitouyang Bay, China, using coastal acoustic tomography. IEEE Journal of Oceanic Engineering, 38(2): 285-296[DOI:10.1109/JOE.2012.2223911]
Zhu X H, Zhang C Z, Wu Q S, Kaneko A, Fan X P and Li B. 2012. Measuring discharge in a river with tidal bores by use of the coastal acoustic tomography system. Estuarine, Coastal and Shelf Science, 104-105: 54-65[DOI:10.1016/j.ecss.2012.03.022]
Zhu Z N, Zhu X H, Zhang C Z, Fan X P, Liao G H, Xuan J L, Long Y, Ma Y L, Zhao R X, He Z G, Zhang T and Zhang X M. 2015. An observational experiment of coastal acoustic tomography to map the structure of tidal currents in Sanmen Bay, China. Chinese Journal of Geophysics, 2015, 58(5): 1742-1753
朱泽南, 朱小华, 张传正, 樊孝鹏, 廖光洪, 宣基亮, 龙钰, 马云龙, 赵瑞祥, 贺治国, 张涛, 章向明. 2015. 三门湾沿海声层析潮流观测实验. 地球物理学报, 58(5): 1742-1753)[DOI:10.6038/cjg20150524]
Zimmer H, Bruhn A and Weickert J. 2011. Freehand HDR imaging of moving scenes with simultaneous resolution enhancement. Computer Graphics Forum, 30(2): 405-414[DOI:10.1111/j.1467-8659.2011.01870.x]
相关文章
相关作者
相关机构
京公网安备11010802024621
