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武汉科技大学 信息科学与工程学院, 湖北 武汉 430081
[ "张洪基(1998—),男,湖北襄阳人,硕士研究生,2020年于武汉科技大学获得学士学位,主要从事光场图像处理、机器学习等方面的研究。E-mail:1363209384@qq.com" ]
[ "邓慧萍(1983—),女,湖北应城人,博士,副教授,2013年于华中科技大学获得博士学位,主要从事3D视频与图像处理、机器学习等方面的研究。E-mail:denghuiping@wust.edu.cn" ]
收稿日期:2022-04-11,
修回日期:2022-04-19,
纸质出版日期:2022-10-05
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张洪基, 邓慧萍, 向森, 等. 基于空间角度解耦融合网络的光场重建[J]. 液晶与显示, 2022,37(10):1345-1354.
Hong-ji ZHANG, Hui-ping DENG, Sen XIANG, et al. Light field reconstruction based on spatial-angular decouple and fuse network[J]. Chinese journal of liquid crystals and displays, 2022, 37(10): 1345-1354.
张洪基, 邓慧萍, 向森, 等. 基于空间角度解耦融合网络的光场重建[J]. 液晶与显示, 2022,37(10):1345-1354. DOI: 10.37188/CJLCD.2022-0117.
Hong-ji ZHANG, Hui-ping DENG, Sen XIANG, et al. Light field reconstruction based on spatial-angular decouple and fuse network[J]. Chinese journal of liquid crystals and displays, 2022, 37(10): 1345-1354. DOI: 10.37188/CJLCD.2022-0117.
密集采样的光场能提供沉浸式的视觉体验,在三维重建和虚拟现实等领域有着广泛的应用。光场重建技术可以从稀疏采样光场获取密集采样光场,但当光场图像的基线增大时,会造成重建视图中边缘的遮挡和纹理重复区域的模糊。本文提出了一种基于空间角度解耦融合的光场重建网络,充分利用光场图像蕴含的深度信息以及空间信息解决上述复杂区域的重建难题。采用多路输入方式来获取光场多方向信息以解决遮挡问题,在每条支路通过空洞空间卷积池化金字塔模块来提取多尺度特征,以获取大接受域的视差信息和上下文信息;设计了空间角度解耦融合模块,利用空间信息指导光场角度超分辨率重建,使重建视图在纹理重复区域更加清晰。实验结果表明,该网络在HCI、HCI old和30 scenes数据集上重建光场的平均PSNR和SSIM分别达到了39.73 dB和0.965,表现出较高的准确性,优于所比较的方法。
The densely sampled light field can provide immersive visual experience and have extensive applications in areas such as 3D reconstruction and virtual reality. Light field reconstruction technology can obtain densely sampled light fields from sparsely sampled light fields, but when the baseline of the light field image increases, it will cause occlusion on edges and textured duplicate areas blur in the reconstructed view. In this paper, we propose a light field reconstruction network based on spatial-anglular decouple and fuse, it can make full use of the depth information and spatial information contained in the light field image and solve the reconstruction problems of the complex regions. The multi-branch input is used to supply the multi-directional information of the light field to solve the occlusion problem. In each branch, multi-scale features are extracted by atrous spatial pyramid pooling module to obtain disparity information and context information for the large receptive fields. The spatial-angular decouple and fuse module is designed to explore the spatial information, guiding the light field angle super-resolution reconstruction so as to obtain the reconstruction view with clearer texture duplicate region. Experimental results show that the average PSNR and SSIM reached 39.73 dB and 0.965 on the HCI, HCI old and 30 scenes datasets,showing high accuracy and outperforming the compared method.
陈苑锋 . 视觉深度估计与点云建图研究进展 [J]. 液晶与显示 , 2021 , 36 ( 6 ): 896 - 911 . doi: 10.37188/CJLCD.2020-0047 http://dx.doi.org/10.37188/CJLCD.2020-0047
CHEN Y F . Progress of visual depth estimation and point cloud mapping [J]. Chinese Journal of Liquid Crystals and Displays , 2021 , 36 ( 6 ): 896 - 911 . (in Chinese) . doi: 10.37188/CJLCD.2020-0047 http://dx.doi.org/10.37188/CJLCD.2020-0047
MENG N , LI K , LIU J Z , et al . Light field view synthesis via aperture disparity and warping confidence map [J]. IEEE Transactions on Image Processing , 2021 , 30 : 3908 - 3921 . doi: 10.1109/tip.2021.3066293 http://dx.doi.org/10.1109/tip.2021.3066293
WU G C , WANG Y Q , LIU Y B , et al . Spatial-angular attention network for light field reconstruction [J]. IEEE Transactions on Image Processing , 2021 , 30 : 8999 - 9013 . doi: 10.1109/tip.2021.3122089 http://dx.doi.org/10.1109/tip.2021.3122089
KO K , KOH Y J , CHANG S , et al . Light field super-resolution via adaptive feature remixing [J]. IEEE Transactions on Image Processing , 2021 , 30 : 4114 - 4128 . doi: 10.1109/tip.2021.3069291 http://dx.doi.org/10.1109/tip.2021.3069291
CHEN A P , XU Z X , ZHAO F Q , et al . MVSNeRF: fast generalizable radiance field reconstruction from multi-view stereo [C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal : IEEE , 2021 : 14104 - 14113 . doi: 10.1109/iccv48922.2021.01386 http://dx.doi.org/10.1109/iccv48922.2021.01386
SHI L X , HASSANIEH H , DAVIS A , et al . Light field reconstruction using sparsity in the continuous Fourier domain [J]. ACM Transactions on Graphics , 2014 , 34 ( 1 ): 12 . doi: 10.1145/2682631 http://dx.doi.org/10.1145/2682631
VAGHARSHAKYAN S , BREGOVIC R , GOTCHEV A . Light field reconstruction using shearlet transform [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018 , 40 ( 1 ): 133 - 147 . doi: 10.1109/tpami.2017.2653101 http://dx.doi.org/10.1109/tpami.2017.2653101
WU G C , ZHAO M D , WANG L Y , et al . Light field reconstruction using deep convolutional network on EPI [C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu : IEEE , 2017 : 1638 - 1646 . doi: 10.1109/cvpr.2017.178 http://dx.doi.org/10.1109/cvpr.2017.178
PENDU M L , GUILLEMOT C , SMOLIC A . A Fourier disparity layer representation for light fields [J]. IEEE Transactions on Image Processing , 2019 , 28 ( 11 ): 5740 - 5753 . doi: 10.1109/tip.2019.2922099 http://dx.doi.org/10.1109/tip.2019.2922099
YEUNG H W F , HOU J H , CHEN J , et al . Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues [C]// Proceedings of the 15th European Conference on Computer Vision (ECCV) . Munich : Springer , 2018 : 138 - 154 . doi: 10.1007/978-3-030-01231-1_9 http://dx.doi.org/10.1007/978-3-030-01231-1_9
MENG N , SO H K H , SUN X , et al . High-dimensional dense residual convolutional neural network for light field reconstruction [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , 43 ( 3 ): 873 - 886 . doi: 10.1109/tpami.2019.2945027 http://dx.doi.org/10.1109/tpami.2019.2945027
MENG N , WU X F , LIU J Z , et al . High-order residual network for light field super-resolution [J]. Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 7 ): 11757 - 11764 . doi: 10.1609/aaai.v34i07.6847 http://dx.doi.org/10.1609/aaai.v34i07.6847
JEON H G , PARK J , CHOE G , et al . Accurate depth map estimation from a lenslet light field camera [C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston : IEEE , 2015 : 1547 - 1555 . doi: 10.1109/cvpr.2015.7298762 http://dx.doi.org/10.1109/cvpr.2015.7298762
WANNER S , GOLDLUECKE B . Variational light field analysis for disparity estimation and super-resolution [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2014 , 36 ( 3 ): 606 - 619 . doi: 10.1109/tpami.2013.147 http://dx.doi.org/10.1109/tpami.2013.147
FLYNN J , NEULANDER I , PHILBIN J , et al . Deep stereo: learning to predict new views from the world's imagery [C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE , 2016 : 5515 - 5524 . doi: 10.1109/cvpr.2016.595 http://dx.doi.org/10.1109/cvpr.2016.595
KALANTARI N K , WANG T C , RAMAMOORTHI R . Learning-based view synthesis for light field cameras [J]. ACM Transactions on Graphics , 2016 , 35 ( 6 ): 193 . doi: 10.1145/2980179.2980251 http://dx.doi.org/10.1145/2980179.2980251
JIN J , HOU J H , YUAN H , et al . Learning light field angular super-resolution via a geometry-aware network [J]. Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 7 ): 11141 - 11148 . doi: 10.1609/aaai.v34i07.6771 http://dx.doi.org/10.1609/aaai.v34i07.6771
WU G C , LIU Y B , DAI Q H , et al . Learning sheared EPI structure for light field reconstruction [J]. IEEE Transactions on Image Processing , 2019 , 28 ( 7 ): 3261 - 3273 . doi: 10.1109/tip.2019.2895463 http://dx.doi.org/10.1109/tip.2019.2895463
ZHOU T H , TUCKER R , FLYNN J , et al . Stereo magnification: learning view synthesis using multiplane images [J]. ACM Transactions on Graphics , 2018 , 37 ( 4 ): 65 . doi: 10.1145/3197517.3201323 http://dx.doi.org/10.1145/3197517.3201323
MILDENHALL B , SRINIVASAN P P , ORTIZ-CAYON R , et al . Local light field fusion: practical view synthesis with prescriptive sampling guidelines [J]. ACM Transactions on Graphics , 2019 , 38 ( 4 ): 29 . doi: 10.1145/3306346.3322980 http://dx.doi.org/10.1145/3306346.3322980
HONAUER K , JOHANNSEN O , KONDERMANN D , et al . A dataset and evaluation methodology for depth estimation on 4D light fields [C]// Proceedings of the 13th Asian Conference on Computer Vision . Taipei, China : Springer , 2017 : 19 - 34 . doi: 10.1007/978-3-319-54187-7_2 http://dx.doi.org/10.1007/978-3-319-54187-7_2
WANNER S , MEISTER S , GOLDLUECKE B . Datasets and benchmarks for densely sampled 4D light fields [M]//BRONSTEIN M, FAVRE J, HORMANN K. Vision, Modeling & Visualization. Geneva : The Eurographics Association , 2013 : 225 - 226 .
KINGMA D P , BA J . Adam: a method for stochastic optimization [J]. arXiv , 2014 : 1412 .6980.
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