Light field imaging for computer vision: a survey

Chen JIA; Fan SHI; Meng ZHAO; Shengyong CHEN

doi:10.1631/FITEE.2100180

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Light field imaging for computer vision: a survey

Regular Papers | Updated：2022-09-08

- Light field imaging for computer vision: a survey
- 用于计算机视觉任务的光场成像技术综述
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 7, Pages: 1077-1097(2022)
- Affiliations：
  
  1.Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), Tianjin University of Technology,Tianjin300384,China
  2.Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology,Tianjin300384,China
- Author bio：
  
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2100180
  CLC： TP391.4
- Received：07 April 2021，
  
  Revised：2021-11-10，
  
  Published Online：12 July 2022，
  
  Published：23 July 2022
- Accepted：
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Chen JIA, Fan SHI, Meng ZHAO, et al. Light field imaging for computer vision: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(7): 1077-1097.
DOI：

Chen JIA, Fan SHI, Meng ZHAO, et al. Light field imaging for computer vision: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(7): 1077-1097. DOI： 10.1631/FITEE.2100180.

摘要

光场成像因其解决计算机视觉问题的能力而备受关注。本文首先简要回顾了近年来计算机视觉的研究进展。对于影响计算机视觉发展的大多数因素来说，视觉信息获取的丰富性和准确性起着决定性作用。光场成像技术利用照相机或微透镜阵列记录光线位置和方向信息，获取完整三维场景信息，为计算机视觉研究做出巨大贡献。光场成像提高了深度估计以及图像分割、融合和三维重建的精度。光场成像还被创新地应用于虹膜和人脸识别、材料和虚假行人识别、极平面图像采集和形状恢复以及光场显微镜。我们进一步总结了光场成像技术在计算机视觉研究中存在的问题和发展趋势，如光场数据集的建立和评估、在高动态范围条件下的应用、光场增强和虚拟现实。光场成像在各种研究中取得巨大成功。在过去25年，超过180篇文献报道了光场成像在解决计算机视觉问题上的能力。我们梳理了这些文献，使研究人员更容易搜索有关解决方案的详细方法。

Abstract

Light field (LF) imaging has attracted attention because of its ability to solve computer vision problems. In this paper we briefly review the research progress in computer vision in recent years. For most factors that affect computer vision development

the richness and accuracy of visual information acquisition are decisive. LF imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays

acquiring complete three-dimensional (3D) scene information. LF imaging technology improves the accuracy of depth estimation

image segmentation

blending

fusion

and 3D reconstruction. LF has also been innovatively applied to iris and face recognition

identification of materials and fake pedestrians

acquisition of epipolar plane images

shape recovery

and LF microscopy. Here

we further summarize the existing problems and the development trends of LF imaging in computer vision

including the establishment and evaluation of the LF dataset

applications under high dynamic range (HDR) conditions

LF image enhancement

virtual reality

3D display

and 3D movies

military optical camouflage technology

image recognition at micro-scale

image processing method based on HDR

and the optimal relationship between spatial resolution and four-dimensional (4D) LF information acquisition. LF imaging has achieved great success in various studies. Over the past 25 years

more than 180 publications have reported the capability of LF imaging in solving computer vision problems. We summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

关键词

Keywords

references

Adelson EH , Bergen JR , 1991 . The plenoptic function and the elements of early vision . In: Landy MS , Movshon JA (Eds.), Computational Models of Visual Processing . MIT Press , Cambridge, USA , p. 3 - 20 .

Afshari H , Akin A , Popovic V , et al. , 2012 . Real-time FPGA implementation of linear blending vision reconstruction algorithm using a spherical light field camera . Proc IEEE Workshop on Signal Processing Systems , p. 49 - 54 . https://doi.org/10.1109/SiPS.2012.49 https://doi.org/10.1109/SiPS.2012.49

Alperovich A , Goldluecke B , 2017 . A variational model for intrinsic light field decomposition . Proc 13 th Asian Conf on Computer Vision , p. 66 - 82 . https://doi.org/10.1007/978-3-319-54187-7_5 https://doi.org/10.1007/978-3-319-54187-7_5

Balogh T , Kovács PT , 2010 . Real-time 3D light field transmission . Proc SPIE 7724, Real-Time Image and Video Processing , Article 772406 . https://doi.org/10.1117/12.854571 https://doi.org/10.1117/12.854571

Berent J , Dragotti PL , 2007 . Segmentation of epipolar-plane image volumes with occlusion and disocclusion competition . Proc IEEE Workshop on Multimedia Signal Processing , p. 182 - 185 . https://doi.org/10.1109/MMSP.2006.285293 https://doi.org/10.1109/MMSP.2006.285293

Broxton M , Grosenick L , Yang S , et al. , 2013 . Wave optics theory and 3-D deconvolution for the light field microscope . Opt Expr , 21 ( 21 ): 25418 - 25439 . https://doi.org/10.1364/OE.21.025418 https://doi.org/10.1364/OE.21.025418

Campbell NDF , Vogiatzis G , Hernández C , et al. , 2010 . Automatic 3D object segmentation in multiple views using volumetric graph-cuts . Image Vis Comput , 28 ( 1 ): 14 - 25 . https://doi.org/10.1016/j.imavis.2008.09.005 https://doi.org/10.1016/j.imavis.2008.09.005

Campbell NDF , Vogiatzis G , Hernandez C , et al. , 2011 . Automatic object segmentation from calibrated images . Proc Conf for Visual Media Production , p. 126 - 137 . https://doi.org/10.1109/CVMP.2011.21 https://doi.org/10.1109/CVMP.2011.21

Chen XY , Dai F , Ma YK , et al. , 2015 . Automatic foreground segmentation using light field images . Proc Visual Communications and Image Processing , p. 1 - 4 . https://doi.org/10.1109/VCIP.2015.7457895 https://doi.org/10.1109/VCIP.2015.7457895

Cheng Z , Xiong ZW , Chen C , et al. , 2019 . Light field super-resolution: a benchmark . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition Workshops , p. 1804 - 1813 . https://doi.org/10.1109/CVPRW.2019.00231 https://doi.org/10.1109/CVPRW.2019.00231

Cohen N , Yang S , Andalman A , et al. , 2014 . Enhancing the performance of the light field microscope using wavefront coding . Opt Expr , 22 ( 20 ): 24817 - 24839 . https://doi.org/10.1364/oe.22.024817 https://doi.org/10.1364/oe.22.024817

Criminisi A , Kang SB , Swaminathan R , et al. , 2005 . Extracting layers and analyzing their specular properties using epipolar-plane-image analysis . Comput Vis Image Underst , 97 ( 1 ): 51 - 85 . https://doi.org/10.1016/j.cviu.2004.06.001 https://doi.org/10.1016/j.cviu.2004.06.001

Cui YL , Yu M , Jiang ZD , et al. , 2021 . Blind light field image quality assessment by analyzing angular-spatial characteristics . Dig Signal Process , 117 : 103138 . https://doi.org/10.1016/j.dsp.2021.103138 https://doi.org/10.1016/j.dsp.2021.103138

Fang YM , Wei KK , Hou JH , et al. , 2018 . Light filed image quality assessment by local and global features of epipolar plane image . Proc IEEE 4 th Int Conf on Multimedia Big Data , p. 1 - 6 . https://doi.org/10.1109/BigMM.2018.8499086 https://doi.org/10.1109/BigMM.2018.8499086

Fiss J , Curless B , Szeliski R , 2014 . Refocusing plenoptic images using depth-adaptive splatting . Proc IEEE Int Conf on Computational Photography , p. 1 - 9 . https://doi.org/10.1109/ICCPHOT.2014.6831809 https://doi.org/10.1109/ICCPHOT.2014.6831809

Gao Q , Han L , Shen J , et al. , 2017 . Focused-region segmentation for light field images based on PCNN . Proc Int Smart Cities Conf , p. 1 - 6 . https://doi.org/10.1109/ISC2.2017.8090851 https://doi.org/10.1109/ISC2.2017.8090851

Georgiev TG , Lumsdaine A , 2010 . Focused plenoptic camera and rendering . J Electron Imag , 19 ( 2 ): 021106 . https://doi.org/10.1117/1.3442712 https://doi.org/10.1117/1.3442712

Gershun A , 1939 . The light field . J Math Phys , 18 ( 1-4 ): 51 - 151 . https://doi.org/10.1002/sapm193918151 https://doi.org/10.1002/sapm193918151

Ghasemi A , Vetterli M , 2014 . Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos . Proc IEEE Int Conf on Acoustics, Speech and Signal Processing , p. 4588 - 4592 . https://doi.org/10.1109/ICASSP.2014.6854471 https://doi.org/10.1109/ICASSP.2014.6854471

Gryaditskaya Y , Masia B , Didyk P , et al. , 2016 . Gloss editing in light fields . Proc Conf on Vision, Modeling and Visualization , p. 127 - 135 . https://doi.org/10.5555/3056901.3056923 https://doi.org/10.5555/3056901.3056923

Guo BC , Wen JT , Han YX , 2020 . Deep material recognition in light-fields via disentanglement of spatial and angular information . Proc 16 th European Conf on Computer Vision , p. 664 - 679 . https://doi.org/10.1007/978-3-030-58586-0_39 https://doi.org/10.1007/978-3-030-58586-0_39

Guo XQ , Lin HT , Yu Z , et al. , 2015 . Barcode imaging using a light field camera . Proc European Conf on Computer Vision , p. 519 - 532 . https://doi.org/10.1007/978-3-319-16181-5_40 https://doi.org/10.1007/978-3-319-16181-5_40

Hog M , Sabater N , Guillemot C , 2016 . Light field segmentation using a ray-based graph structure . Proc 14 th European Conf on Computer Vision , p. 35 - 50 . https://doi.org/10.1007/978-3-319-46478-7_3 https://doi.org/10.1007/978-3-319-46478-7_3

Hsieh PY , Chou PY , Lin HA , et al. , 2018 . Long working range light field microscope with fast scanning multifocal liquid crystal microlens array . Opt Expr , 26 ( 8 ): 10981 - 10996 . https://doi.org/10.1364/oe.26.010981 https://doi.org/10.1364/oe.26.010981

Huang ZJ , Yu M , Xu HY , et al. , 2018 . New quality assessment method for dense light fields . Proc SPIE 10817, Optoelectronic Imaging and Multimedia Technology V , Article 1081717 . https://doi.org/10.1117/12.2502277 https://doi.org/10.1117/12.2502277

Jia C , Shi F , Zhao YF , et al. , 2018 . Identification of pedestrians from confused planar objects using light field imaging . IEEE Access , 6 : 39375 - 39384 . https://doi.org/10.1109/ACCESS.2018.2855723 https://doi.org/10.1109/ACCESS.2018.2855723

Johannsen O , Sulc A , Goldluecke B , 2015 . Variational separation of light field layers . Proc 20 th Int Symp on Vision, Modeling, and Visualization , p. 135 - 142 . https://doi.org/10.2312/vmv.20151268 https://doi.org/10.2312/vmv.20151268

Kalantari NK , Wang TC , Ramamoorthi R , 2016 . Learning-based view synthesis for light field cameras . ACM Trans Graph , 35 ( 6 ): 193 . https://doi.org/10.1145/2980179.2980251 https://doi.org/10.1145/2980179.2980251

Kim C , Zimmer H , Pritch Y , et al. , 2013 . Scene reconstruction from high spatio-angular resolution light fields . ACM Trans Graph , 32 ( 4 ): 73 . https://doi.org/10.1145/2461912.2461926 https://doi.org/10.1145/2461912.2461926

Lee JY , Park RH , 2017 . Separation of foreground and background from light field using gradient information . Appl Opt , 56 ( 4 ): 1069 - 1078 . https://doi.org/10.1364/AO.56.001069 https://doi.org/10.1364/AO.56.001069

Levoy M , Hanrahan P , 1996 . Light field rendering . Proc 23 rd Annual Conf on Computer Graphics and Interactive Techniques , p. 31 - 42 . https://doi.org/10.1145/237170.237199 https://doi.org/10.1145/237170.237199

Levoy M , Ng R , Adams A , et al. , 2006 . Light field microscopy . Proc ACM SIGGRAPH , p. 924 - 934 . https://doi.org/10.1145/1179352.1141976 https://doi.org/10.1145/1179352.1141976

Li NY , Ye JW , Ji Y , et al. , 2014 . Saliency detection on light field . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 2806 - 2813 . https://doi.org/10.1109/CVPR.2014.359 https://doi.org/10.1109/CVPR.2014.359

Li NY , Sun BL , Yu JY , 2015 . A weighted sparse coding framework for saliency detection . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 5216 - 5223 . https://doi.org/10.1109/CVPR.2015.7299158 https://doi.org/10.1109/CVPR.2015.7299158

Li ZQ , Xu ZX , Ramamoorthi R , et al. , 2017 . Robust energy minimization for BRDF-invariant shape from light fields . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 578 - 586 . https://doi.org/10.1109/CVPR.2017.69 https://doi.org/10.1109/CVPR.2017.69

Liang CK , Lin TH , Wong BY , et al. , 2008 . Programmable aperture photography: multiplexed light field acquisition . ACM Trans Graph , 27 ( 3 ): 1 - 10 . https://doi.org/10.1145/1360612.1360654 https://doi.org/10.1145/1360612.1360654

Lippmann G , 1908 . Epreuves reversibles, photographies integrales . Comput R Acad Sci , 444 : 446 - 451 .

Lumsdaine A , Georgiev T , 2009 . The focused plenoptic camera . Proc IEEE Int Conf on Computational Photography , p. 1 - 8 . https://doi.org/10.1109/ICCPHOT.2009.5559008 https://doi.org/10.1109/ICCPHOT.2009.5559008

Lv XQ , Wang X , Wang Q , et al. , 2021 . 4D light field segmentation from light field super-pixel hypergraph representation . IEEE Trans Vis Comput Graph , 27 ( 9 ): 3597 - 3610 . https://doi.org/10.1109/TVCG.2020.2982158 https://doi.org/10.1109/TVCG.2020.2982158

Marquez M , Rueda-Chacon H , Arguello H , 2020 . Compressive spectral light field image reconstruction via online tensor representation . IEEE Trans Image Process , 29 : 3558 - 3568 . https://doi.org/10.1109/TIP.2019.2963376 https://doi.org/10.1109/TIP.2019.2963376

Mehajabin N , Pourazad M , Nasiopoulos P , 2020 . SSIM assisted pseudo-sequence-based prediction structure for light field video compression . Proc IEEE Int Conf on Consumer Electronics , p. 1 - 2 . https://doi.org/10.1109/ICCE46568.2020.9042968 https://doi.org/10.1109/ICCE46568.2020.9042968

Meng CL , An P , Huang XP , et al. , 2019 . Objective quality assessment for light field based on refocus characteristic . Proc 10 th Int Conf on Image and Graphics , p. 193 - 204 . https://doi.org/10.1007/978-3-030-34113-8_17 https://doi.org/10.1007/978-3-030-34113-8_17

Mihara H , Funatomi T , Tanaka K , et al. , 2016 . 4D light field segmentation with spatial and angular consistencies . Proc IEEE Int Conf on Computational Photography , p.‍ 1 - 8 . https://doi.org/10.1109/ICCPHOT.2016.7492872 https://doi.org/10.1109/ICCPHOT.2016.7492872

Murgia F , Giusto D , Perra C , et al. , 2015 . 3D reconstruction from plenoptic image . Proc 23 rd Telecommunications Forum Telfor , p. 448 - 451 . https://doi.org/10.1109/TELFOR.2015.7377504 https://doi.org/10.1109/TELFOR.2015.7377504

Ng R , Levoy M , Brédif M , et al. , 2005 . Light field photography with a hand-held plenoptic camera . Stanford Tech Report CTSR 2005-02 .

Nian ZC , Jung C , 2019 . CNN-based multi-focus image fusion with light field data . Proc IEEE Int Conf on Image Processing , p. 1044 - 1048 . https://doi.org/10.1109/ICIP.2019.8803065 https://doi.org/10.1109/ICIP.2019.8803065

Paudyal P , Olsson R , Sjöström M , et al. , 2016 . SMART: a light field image quality dataset . Proc 7 th Int Conf on Multimedia Systems , Article 49 . https://doi.org/10.1145/2910017.2910623 https://doi.org/10.1145/2910017.2910623

Paudyal P , Battisti F , Sjöström M , et al. , 2017 . Towards the perceptual quality evaluation of compressed light field images . IEEE Trans Broadcast , 63 ( 3 ): 507 - 522 . https://doi.org/10.1109/TBC.2017.2704430 https://doi.org/10.1109/TBC.2017.2704430

Paudyal P , Battisti F , Carli M , 2019 . Reduced reference quality assessment of light field images . IEEE Trans Broadcast , 65 ( 1 ): 152 - 165 . https://doi.org/10.1109/TBC.2019.2892092 https://doi.org/10.1109/TBC.2019.2892092

Piao YR , Li X , Zhang M , et al. , 2019a . Saliency detection via depth-induced cellular automata on light field . IEEE Trans Image Process , 29 : 1879 - 1889 . https://doi.org/10.1109/TIP.2019.2942434 https://doi.org/10.1109/TIP.2019.2942434

Piao YR , Rong ZK , Zhang M , et al. , 2019b . Deep light-field-driven saliency detection from a single view . Proc 28 th Int Joint Conf on Artificial Intelligence , p. 904 - 911 . https://doi.org/10.24963/ijcai.2019/127 https://doi.org/10.24963/ijcai.2019/127

Piao YR , Jiang YY , Zhang M , et al. , 2021 . PANet: patch-aware network for light field salient object detection . IEEE Trans Cybern , early access . https://doi.org/10.1109/TCYB.2021.3095512 https://doi.org/10.1109/TCYB.2021.3095512

Raghavendra R , Busch C , 2014 . Presentation attack detection on visible spectrum iris recognition by exploring inherent characteristics of light field camera . Proc IEEE Int Joint Conf on Biometrics , p. 1 - 8 . https://doi.org/10.1109/BTAS.2014.6996226 https://doi.org/10.1109/BTAS.2014.6996226

Raghavendra R , Raja KB , Yang B , et al. , 2013a . Combining iris and periocular recognition using light field camera . Proc 2 nd IAPR Asian Conf on Pattern Recognition , p. 155 - 159 . https://doi.org/10.1109/ACPR.2013.22 https://doi.org/10.1109/ACPR.2013.22

Raghavendra R , Raja KB , Yang B , et al. , 2013b . A novel image fusion scheme for robust multiple face recognition with light-field camera . Proc 16 th Int Conf on Information Fusion , p. 722 - 729 .

Raghavendra R , Raja KB , Busch C , 2016 . Exploring the usefulness of light field cameras for biometrics: an empirical study on face and iris recognition . IEEE Trans Inform Forens Secur , 11 ( 5 ): 922 - 936 . https://doi.org/10.1109/TIFS.2015.2512559 https://doi.org/10.1109/TIFS.2015.2512559

Rerabek M , Ebrahimi T , 2016 . New light field image dataset . Proc 8 th Int Conf on Quality of Multimedia Experience .

Sabater N , Boisson G , Vandame B , et al. , 2017 . Dataset and pipeline for multi-view light-field video . Proc IEEE Conf on Computer Vision and Pattern Recognition Workshops , p. 1743 - 1753 . https://doi.org/10.1109/CVPRW.2017.221 https://doi.org/10.1109/CVPRW.2017.221

Sepas-Moghaddam A , Pereira F , Correia PL , 2018 . Light field-based face presentation attack detection: reviewing, benchmarking and one step further . IEEE Trans Inform Forens Secur , 13 ( 7 ): 1696 - 1709 . https://doi.org/10.1109/TIFS.2018.2799427 https://doi.org/10.1109/TIFS.2018.2799427

Shan L , An P , Meng CL , et al. , 2019 . A no-reference image quality assessment metric by multiple characteristics of light field images . IEEE Access , 7 : 127217 - 127229 . https://doi.org/10.1109/ACCESS.2019.2940093 https://doi.org/10.1109/ACCESS.2019.2940093

Sheng H , Deng SY , Zhang S , et al. , 2016 . Segmentation of light field image with the structure tensor . Proc IEEE Int Conf on Image Processing , p. 1469 - 1473 . https://doi.org/10.1109/ICIP.2016.7532602 https://doi.org/10.1109/ICIP.2016.7532602

Shi LK , Zhao SY , Zhou W , et al. , 2018 . Perceptual evaluation of light field image . Proc 25 th IEEE Int Conf on Image Processing , p. 41 - 45 . https://doi.org/10.1109/ICIP.2018.8451077 https://doi.org/10.1109/ICIP.2018.8451077

Shi LK , Zhao SY , Chen ZB , 2019 . Belif: blind quality evaluator of light field image with tensor structure variation index . Proc IEEE Int Conf on Image Processing , p. 3781 - 3785 . https://doi.org/10.1109/ICIP.2019.8803559 https://doi.org/10.1109/ICIP.2019.8803559

Shi LK , Zhou W , Chen ZB , et al. , 2020 . No-reference light field image quality assessment based on spatial-angular measurement . IEEE Trans Circ Syst Video Technol , 30 ( 11 ): 4114 - 4128 . https://doi.org/10.1109/TCSVT.2019.2955011 https://doi.org/10.1109/TCSVT.2019.2955011

Smith BM , Zhang L , Jin HL , et al. , 2009 . Light field video stabilization . Proc IEEE 12 th Int Conf on Computer Vision , p. 341 - 348 . https://doi.org/10.1109/ICCV.2009.5459270 https://doi.org/10.1109/ICCV.2009.5459270

Sulc A , Alperovich A , Marniok N , et al. , 2016 . Reﬂection separation in light fields based on sparse coding and specular flow . Proc Conf on Vision, Modeling and Visualization , p. 137 - 144 . https://doi.org/10.5555/3056901.3056924 https://doi.org/10.5555/3056901.3056924

Sun J , Hossain M , Xu CL , et al. , 2017 . A novel calibration method of focused light field camera for 3-D reconstruction of flame temperature . Opt Commun , 390 : 7 - 15 . https://doi.org/10.1016/j.optcom.2016.12.056 https://doi.org/10.1016/j.optcom.2016.12.056

Tambe S , Veeraraghavan A , Agrawal A , 2013 . Towards motion aware light field video for dynamic scenes . Proc IEEE Int Conf on Computer Vision , p. 1009 - 1016 . https://doi.org/10.1109/ICCV.2013.129 https://doi.org/10.1109/ICCV.2013.129

Tao MW , Srinivasan PP , Malik J , et al. , 2015a . Depth from shading, defocus, and correspondence using light-field angular coherence . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 1940 - 1948 . https://doi.org/10.1109/CVPR.2015.7298804 https://doi.org/10.1109/CVPR.2015.7298804

Tao MW , Su JC , Wang TC , et al. , 2015b . Depth estimation and specular removal for glossy surfaces using point and line consistency with light-field cameras . IEEE Trans Patt Anal Mach Intell , 38 ( 6 ): 1155 - 1169 . https://doi.org/10.1109/TPAMI.2015.2477811 https://doi.org/10.1109/TPAMI.2015.2477811

Tian Y , Zeng HQ , Hou JH , et al. , 2021 . A light field image quality assessment model based on symmetry and depth features . IEEE Trans Circ Syst Video Technol , 31 ( 5 ): 2046 - 2050 . https://doi.org/10.1109/TCSVT.2020.2971256 https://doi.org/10.1109/TCSVT.2020.2971256

Vizcaíno JP , Saltarin F , Belyaev Y , et al. , 2021 . Learning to reconstruct confocal microscopy stacks from single light field images . IEEE Trans Comput Imag , 7 : 775 - 788 . https://doi.org/10.1109/TCI.2021.3097611 https://doi.org/10.1109/TCI.2021.3097611

Wang AZ , Wang MH , Li XY , et al. , 2017 . A two-stage Bayesian integration framework for salient object detection on light field . Neur Process Lett , 46 ( 3 ): 1083 - ‍1094 . https://doi.org/10.1007/s11063-017-9610-x https://doi.org/10.1007/s11063-017-9610-x

Wang HQ , Xu CX , Wang XZ , et al. , 2016 . Light field imaging based accurate image specular highlight removal . PLoS ONE , 11 ( 6 ): e0156173 . https://doi.org/10.1371/journal.pone.0156173 https://doi.org/10.1371/journal.pone.0156173

Wang TC , Efros AA , Ramamoorthi R , 2015 . Occlusion-aware depth estimation using light-field cameras . Proc IEEE Int Conf on Computer Vision , p. 3487 - 3495 . https://doi.org/10.1109/ICCV.2015.398 https://doi.org/10.1109/ICCV.2015.398

Wang TC , Zhu JY , Hiroaki E , et al. , 2016a . A 4D light-field dataset and CNN architectures for material recognition . Proc 14 th European Conf on Computer Vision , p. 121 - 138 . https://doi.org/10.1007/978-3-319-46487-9_8 https://doi.org/10.1007/978-3-319-46487-9_8

Wang TC , Chandraker M , Efros AA , et al. , 2016b . SVBRDF-invariant shape and reflectance estimation from light-field cameras . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 5451 - 5459 . https://doi.org/10.1109/CVPR.2016.588 https://doi.org/10.1109/CVPR.2016.588

Wang TC , Zhu JY , Kalantari NK , et al. , 2017 . Light field video capture using a learning-based hybrid imaging system . ACM Trans Graph , 36 ( 4 ): 133 . https://doi.org/10.1145/3072959.3073614 https://doi.org/10.1145/3072959.3073614

Wang TT , Piao YR , Li XC , et al. , 2019 . Deep learning for light field saliency detection . Proc IEEE/CVF Int Conf on Computer Vision , p. 8837 - 8847 . https://doi.org/10.1109/ICCV.2019.00893 https://doi.org/10.1109/ICCV.2019.00893

Wang YQ , Yang JG , Xiao C , et al. , 2018 . An efficient method for the fusion of light field refocused images . Proc SPIE 9 th Int Conf on Graphic and Image Processing , Article 1061536 . https://doi.org/10.1117/12.2302687 https://doi.org/10.1117/12.2302687

Wanner S , Meister S , Goldluecke B , 2013a . Datasets and benchmarks for densely sampled 4D light fields . Proc 18 th Int Workshop on Vision, Modeling, and Visualization , p. 225 - 226 . https://doi.org/10.2312/PE.VMV.VMV13.225-226 https://doi.org/10.2312/PE.VMV.VMV13.225-226

Wanner S , Straehle C , Goldluecke B , 2013b . Globally consistent multi-label assignment on the ray space of 4D light fields . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 1011 - 1018 . https://doi.org/10.1109/CVPR.2013.135 https://doi.org/10.1109/CVPR.2013.135

Wilburn B , Smulski M , Lee HHK , et al. , 2002 . Light field video camera . Proc SPIE 6474, Media Processors , p. 29 - 36 . https://doi.org/10.1117/12.451074 https://doi.org/10.1117/12.451074

Wilburn B , Joshi N , Vaish V , et al. , 2005 . High performance imaging using large camera arrays . ACM Trans Graph , 24 ( 3 ): 765 - 776 . https://doi.org/10.1145/1073204.1073259 https://doi.org/10.1145/1073204.1073259

Wu GC , Masia B , Jarabo A , et al. , 2017 . Light field image processing: an overview . IEEE J Sel Top Signal Process , 11 ( 7 ): 926 - 954 . https://doi.org/10.1109/JSTSP.2017.2747126 https://doi.org/10.1109/JSTSP.2017.2747126

Xu YC , Nagahara H , Shimada A , et al. , 2015 . TransCut: transparent object segmentation from a light-field image . Proc IEEE Int Conf on Computer Vision , p. 3442 - 3450 . https://doi.org/10.1109/ICCV.2015.393 https://doi.org/10.1109/ICCV.2015.393

Xu YC , Nagahara H , Shimada A , et al. , 2019 . TransCut2: transparent object segmentation from a light-field image . IEEE Trans Comput Imag , 5 ( 3 ): 465 - 477 . https://doi.org/10.1109/TCI.2019.2893820 https://doi.org/10.1109/TCI.2019.2893820

Yang JC , 2000 . A Light Field Camera for Image Based Rendering . MS Thesis, Massachusetts Institute of Technology , Cambridge, USA .

Yücer K , Sorkine-Hornung A , Wang O , et al. , 2016 . Efficient 3D object segmentation from densely sampled light fields with applications to 3D reconstruction . ACM Trans Graph , 35 ( 3 ): 22 . https://doi.org/10.1145/2876504 https://doi.org/10.1145/2876504

Zhang C , Chen T , 2004 . A self-reconfigurable camera array . Proc ACM SIGGRAPH Sketches , p. 151 . https://doi.org/10.1145/1186223.1186412 https://doi.org/10.1145/1186223.1186412

Zhang C , Hou GQ , Sun ZA , et al. , 2013 . Light field photography for iris image acquisition . Proc 8 th Chinese Conf on Biometric Recognition , p. 345 - 352 . https://doi.org/10.1007/978-3-319-02961-0_43 https://doi.org/10.1007/978-3-319-02961-0_43

Zhang J , Wang M , Gao J , et al. , 2015 . Saliency detection with a deeper investigation of light field . Proc 24 th Int Joint Conf on Artificial Intelligence , p. 2212 - 2218 .

Zhang J , Wang M , Lin L , et al. , 2017 . Saliency detection on light field: a multi-cue approach . ACM Trans Multim Comput Commun Appl , 13 ( 3 ): 32 . https://doi.org/10.1145/3107956 https://doi.org/10.1145/3107956

Zhang J , Liu YM , Zhang SP , et al. , 2020 . Light field saliency detection with deep convolutional networks . IEEE Trans Image Process , 29 : 4421 - 4434 . https://doi.org/10.1109/TIP.2020.2970529 https://doi.org/10.1109/TIP.2020.2970529

Zhang M , Geng Z , Pei RJ , et al. , 2017 . Three-dimensional light field microscope based on a lenslet array . Opt Commun , 403 : 133 - 142 . https://doi.org/10.1016/j.optcom.2017.07.026 https://doi.org/10.1016/j.optcom.2017.07.026

Zhang M , Li JJ , Wei J , et al. , 2019 . Memory-oriented decoder for light field salient object detection . Proc Advances in Neural Information Processing Systems32 , p. 2898 - 2909 .

Zhang XD , Wang Y , Zhang J , et al. , 2015 . Light field saliency vs. 2D saliency: a comparative study . Neurocomputing , 166 : 389 - 396 . https://doi.org/10.1016/j.neucom.2015.03.042 https://doi.org/10.1016/j.neucom.2015.03.042

Zhou MY , Ding YQ , J Y i et al. , 2020 . Shape and reflectance reconstruction using concentric multi-spectral light field . IEEE Trans Patt Anal Mach Intell , 42 ( 7 ):‍ 1594 - 1605 . https://doi.org/10.1109/TPAMI.2020.2986764 https://doi.org/10.1109/TPAMI.2020.2986764

Zhou W , Shi LK , Chen ZB , et al. , 2020 . Tensor oriented no-reference light field image quality assessment . IEEE Trans Image Process , 29 : 4070 - 4084 . https://doi.org/10.1109/TIP.2020.2969777 https://doi.org/10.1109/TIP.2020.2969777

Zhu H , Zhang Q , Wang Q , 2017 . 4D light field superpixel and segmentation . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 6709 - 6717 . https://doi.org/10.1109/CVPR.2017.710 https://doi.org/10.1109/CVPR.2017.710

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