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摘要
相机阵列是获取空间中目标光场信息的重要手段, 采用大规模密集相机阵列获取高角度分辨率光场的方法增加了采样难度和设备成本, 同时产生的大量数据的同步和传输需求也限制了光场采样规模. 为了实现稀疏光场采样的稠密重建, 本文基于稀疏光场数据, 分析同一场景多视角图像的空间、角度信息的关联性和冗余性, 建立有效的光场字典学习和稀疏编码数学模型, 并根据稀疏编码元素间的约束关系, 建立虚拟角度图像稀疏编码恢复模型, 提出变换域稀疏编码恢复方法, 并结合多场景稠密重建实验, 验证提出方法的有效性. 实验结果表明, 本文方法能够对场景中的遮挡、阴影以及复杂的光影变化信息进行高质量恢复, 可以用于复杂场景的稀疏光场稠密重建. 本研究实现了线性采集稀疏光场的稠密重建, 未来将针对非线性采集稀疏光场的稠密重建进行研究, 以推进光场成像在实际工程中的应用.-
关键词:
- 光场 /
- 字典学习 /
- 稀疏编码 /
- 稠密重建
Abstract
The camera array is an important tool to obtain the light field of target in space. The method of obtaining high angular resolution light field by a large-scaled dense camera array increases the difficulty of sampling and the equipment cost. At the same time, the demand for synchronization and transmission of a large number of data also limits the sampling rate of light field. In order to complete the dense reconstruction of sparse sampling of light field, we analyze the correlation and redundancy of multi-view images in the same scene based on sparse light field data, then establish an effective mathematical model of light field dictionary learning and sparse coding. The trained light field atoms can sparsely express the local spatial-angular consistency of light field, and the four-dimensional (4D) light field patches can be reconstructed from a two-dimensional (2D) local image patch centered around each pixel in the sensor. The global and local constraints of the four-dimensional light field are mapped into the low-dimensional space by the dictionary. These constraints are shown as the sparsity of each vector in the sparse representation domain, the constraints between the positions of non-zero elements and their values. According to the constraints among sparse encoding elements, we establish the sparse encoding recovering model of virtual angular image, and propose the sparse encoding recovering method in the transform domain. The atoms of light field in dictionary are screened and the patches of light field are represented linearly by the sparse representation matrix of the virtual angular image. In the end, the virtual angular images are constructed by image fusion after sparse inverse transform. According to multi-scene dense reconstruction experiments, the effectiveness of the proposed method is verified. The experimental results show that the proposed method can recover the occlusion, shadow and complex illumination in satisfying quality. That is to say, it can be used for dense reconstruction of sparse light field in complex scene. In our study, the dense reconstruction of linear sparse light field is achieved. In the future, the dense reconstruction of nonlinear sparse light field will be studied to promote the practical application of light field imaging.-
Keywords:
- light field /
- dictionary learning /
- sparse coding /
- dense reconstruction
作者及机构信息
Authors and contacts
文章全文 : translate this paragraph
参考文献
[1] Cao X, Zheng G, Li T T 2014 Opt. Express. 22 24081 Google Scholar
[2] Schedl D C, Birklbauer C, Bimber O 2018 Comput. Vis. Image Und. 168 93 Google Scholar
[3] Smolic A, Kauff P 2005 Proc. IEEE 93 98 Google Scholar
[4] McMillan L, Bishop G 1995, Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques Los Angeles, USA, August 6−11, 1995 p39
[5] Fehn C 2004 The International Society for Optical Engineering Bellingham, USA, December 30, 2004 p93
[6] Xu Z, Bi S, Sunkavalli K, Hadap S, Su H, Ramamoorthi R 2019 ACM T. Graphic 38 76
[7] Wang C, Liu X F, Yu W K, Yao X R, Zheng F, Dong Q, Lan R M, Sun Z B, Zhai G J, Zhao Q 2017 Chin. Phys. Lett. 34 104203 Google Scholar
[8] Zhang L, Tam W J 2005 IEEE Trans. Broadcast. 51 191 Google Scholar
[9] Chen W, Chang Y, Lin S, Ding L, Chen L 2005 IEEE Conference on Multimedia and Expo Amsterdam, The Netherlands, July 6−8, 2005 p1314
[10] Jung K H, Park Y K, Kim J K, Lee H, Kim J 2008 3 DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video Istanbul, Turkey, May 28−30, 2008 p237
[11] Hosseini Kamal M, Heshmat B, Raskar R, Vandergheynst P, Wetzstein G 2016 Comput. Vis. Image Und. 145 172 Google Scholar
[12] Levoy M, Hanrahan P 1996 Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques New York, USA, August 4−9, 1996 p31
[13] Levoy M 2006 Computer 39 46 Google Scholar
[14] Donoho D L 2006 IEEE T. Inform. Theory. 52 1289 Google Scholar
[15] Park J Y, Wakin M B 2012 Eurasip J. Adv. Sig. Pr. 2012 37
[16] Zhu B, Liu J Z, Cauley S F, Rosen B R, Rosen M S 2018 Nature 555 487 Google Scholar
[17] Ophir B, Lustig M, Elad M 2011 IEEE J. Sel. Top. Signal Process. 5 1014 Google Scholar
[18] Marwah K, Wetzstein G, Bando Y, Raskar R 2013 ACM T. Graphic. 32 46
[19] Marwah K, Wetzstein G, Veeraraghavan A, Raskar R 2012 ACM SIGGRAPH 2012 Talks Los Angeles, USA, August 5−9, 2012 p42
[20] Tenenbaum J B, Silva V D, Langford J C 2000 Science 290 2319 Google Scholar
[21] Honauer K, Johannsen O, Kondermann D, Glodluecke B 2016 Asian Conference on Computer Vision Taipei, China, November 20−24, 2016 p19
[22] Bottou L, Bousquet O 2008 Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems Whistler, Canada, December 3−6, 2007 p161
[23] Mairal J, Bach F, Ponce J, Sapiro G 2009 Proceedings of the 26th Annual International Conference on Machine Learning Montreal, Canada, June 14–18, 2009 p689
[24] Flynn J, Neulander I, Philbin J, Snavely N 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, USA, June 27−30, 2016 p5515
施引文献
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图 1 算法架构图
Fig. 1. Algorithm workflow.
图 2 光场过完备字典
Fig. 2. Light field overcomplete dictionary.
图 3 重建图像质量曲线图 (a) pixels为256 × 256, 不同稀疏度重建性能曲线图; (b) pixels为512 × 512, 不同稀疏度重建性能曲线图; (c) 不同分辨率重建图像的PSNR曲线图; (d) pixels为256 × 256, 不同冗余度重建性能曲线图
Fig. 3. Performance of reconstructed image: (a) Performance in sparsity, pixels = 256 × 256; (b) performance in sparsity, pixels = 512 × 512; (c) PSNR in different resolution; (d) performance in redundancy, pixels = 256 × 256.
图 4 不同稀疏度、冗余度参数重建图像 (a) K = 16, N = 256; (b) K = 34, N = 1024
Fig. 4. Image reconstruction in different sparsity and redundancy: (a) K = 16, N = 256; (b) K = 34, N = 1024
图 5 包含遮挡目标的稠密光场恢复 (a) 稠密光场; (b), (e) 参考图像; (c), (d) 恢复的view 2, view 5虚拟角度图像; (g), (h)目标图像; (f), (i) 残差图
Fig. 5. Dense reconstruction of light field with occluded targets: (a) Dense light field; (b), (e) reference images; (c), (d) reconstructed virtual images of view 2 and view 5; (g), (h) target images; (f), (i) residual images.
图 6 稠密光场恢复 (a) 本文算法恢复图像; (b) DIBR算法恢复图像; (c) 目标图像; (d) 残差图; (e)稠密光场
Fig. 6. Dense reconstruction of light field: (a) Reconstructed image for proposed algorithm; (b) reconstructed image for DIBR; (c) target image; (d) residual image; (e) dense light field.
表 1 不同稀疏度、冗余度重建图像质量指标
Table 1. Performance of image reconstruction in different sparsity and redundancy
Sparse
parameter (K),
Redundancy
parameter (N)MSE PSNR/dB SSIM Time/s K = 16, N = 256 54.4215 30.7731 0.8860 1266.08 K = 34, N = 1024 49.0044 31.2285 0.8865 14306.55 表 2 不同场景光场稠密重建结果
Table 2. Dense reconstruction of light field in different scenes.
Sense Table Bicycle town Boardgames rosemary Vinyl bicycle* MSE 21.2124 54.4215 25.8005 53.9244 18.8950 22.4756 49.0044 PSNR/dB 34.8649 30.7731 34.0145 30.8129 35.3673 34.6137 31.2285 SSIM 0.9323 0.8860 0.9474 0.9341 0.9699 0.9421 0.8865 * 稀疏度K = 34, 冗余度N = 1024. 深圳SEO优化公司兴安盟网站改版公司宜宾网站推广报价上海网站优化软件报价兴安盟网站排名优化推荐安阳优秀网站设计哪家好郑州SEO按天收费哪家好襄阳SEO按天扣费公司上海百度关键词包年推广多少钱宝安高端网站设计哪家好防城港设计公司网站广东设计网站价格宝安seo排名报价泰安营销网站推荐大浪seo网站优化公司衢州网站搭建多少钱舟山英文网站建设价格廊坊seo网站推广哪家好成都优化公司台州网站优化软件推荐文山SEO按效果付费价格中山网站制作和田企业网站制作多少钱安阳SEO按效果付费公司大鹏网站优化推广多少钱崇左SEO按天收费蚌埠如何制作网站公司吉安网站搜索优化哪家好兴安盟关键词排名包年推广哪家好肇庆网页制作哪家好贵阳网站搜索优化歼20紧急升空逼退外机英媒称团队夜以继日筹划王妃复出草木蔓发 春山在望成都发生巨响 当地回应60岁老人炒菠菜未焯水致肾病恶化男子涉嫌走私被判11年却一天牢没坐劳斯莱斯右转逼停直行车网传落水者说“没让你救”系谣言广东通报13岁男孩性侵女童不予立案贵州小伙回应在美国卖三蹦子火了淀粉肠小王子日销售额涨超10倍有个姐真把千机伞做出来了近3万元金手镯仅含足金十克呼北高速交通事故已致14人死亡杨洋拄拐现身医院国产伟哥去年销售近13亿男子给前妻转账 现任妻子起诉要回新基金只募集到26元还是员工自购男孩疑遭霸凌 家长讨说法被踢出群充个话费竟沦为间接洗钱工具新的一天从800个哈欠开始单亲妈妈陷入热恋 14岁儿子报警#春分立蛋大挑战#中国投资客涌入日本东京买房两大学生合买彩票中奖一人不认账新加坡主帅:唯一目标击败中国队月嫂回应掌掴婴儿是在赶虫子19岁小伙救下5人后溺亡 多方发声清明节放假3天调休1天张家界的山上“长”满了韩国人?开封王婆为何火了主播靠辱骂母亲走红被批捕封号代拍被何赛飞拿着魔杖追着打阿根廷将发行1万与2万面值的纸币库克现身上海为江西彩礼“减负”的“试婚人”因自嘲式简历走红的教授更新简介殡仪馆花卉高于市场价3倍还重复用网友称在豆瓣酱里吃出老鼠头315晚会后胖东来又人满为患了网友建议重庆地铁不准乘客携带菜筐特朗普谈“凯特王妃P图照”罗斯否认插足凯特王妃婚姻青海通报栏杆断裂小学生跌落住进ICU恒大被罚41.75亿到底怎么缴湖南一县政协主席疑涉刑案被控制茶百道就改标签日期致歉王树国3次鞠躬告别西交大师生张立群任西安交通大学校长杨倩无缘巴黎奥运
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[1] Cao X, Zheng G, Li T T 2014 Opt. Express. 22 24081 Google Scholar
[2] Schedl D C, Birklbauer C, Bimber O 2018 Comput. Vis. Image Und. 168 93 Google Scholar
[3] Smolic A, Kauff P 2005 Proc. IEEE 93 98 Google Scholar
[4] McMillan L, Bishop G 1995, Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques Los Angeles, USA, August 6−11, 1995 p39
[5] Fehn C 2004 The International Society for Optical Engineering Bellingham, USA, December 30, 2004 p93
[6] Xu Z, Bi S, Sunkavalli K, Hadap S, Su H, Ramamoorthi R 2019 ACM T. Graphic 38 76
[7] Wang C, Liu X F, Yu W K, Yao X R, Zheng F, Dong Q, Lan R M, Sun Z B, Zhai G J, Zhao Q 2017 Chin. Phys. Lett. 34 104203 Google Scholar
[8] Zhang L, Tam W J 2005 IEEE Trans. Broadcast. 51 191 Google Scholar
[9] Chen W, Chang Y, Lin S, Ding L, Chen L 2005 IEEE Conference on Multimedia and Expo Amsterdam, The Netherlands, July 6−8, 2005 p1314
[10] Jung K H, Park Y K, Kim J K, Lee H, Kim J 2008 3 DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video Istanbul, Turkey, May 28−30, 2008 p237
[11] Hosseini Kamal M, Heshmat B, Raskar R, Vandergheynst P, Wetzstein G 2016 Comput. Vis. Image Und. 145 172 Google Scholar
[12] Levoy M, Hanrahan P 1996 Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques New York, USA, August 4−9, 1996 p31
[13] Levoy M 2006 Computer 39 46 Google Scholar
[14] Donoho D L 2006 IEEE T. Inform. Theory. 52 1289 Google Scholar
[15] Park J Y, Wakin M B 2012 Eurasip J. Adv. Sig. Pr. 2012 37
[16] Zhu B, Liu J Z, Cauley S F, Rosen B R, Rosen M S 2018 Nature 555 487 Google Scholar
[17] Ophir B, Lustig M, Elad M 2011 IEEE J. Sel. Top. Signal Process. 5 1014 Google Scholar
[18] Marwah K, Wetzstein G, Bando Y, Raskar R 2013 ACM T. Graphic. 32 46
[19] Marwah K, Wetzstein G, Veeraraghavan A, Raskar R 2012 ACM SIGGRAPH 2012 Talks Los Angeles, USA, August 5−9, 2012 p42
[20] Tenenbaum J B, Silva V D, Langford J C 2000 Science 290 2319 Google Scholar
[21] Honauer K, Johannsen O, Kondermann D, Glodluecke B 2016 Asian Conference on Computer Vision Taipei, China, November 20−24, 2016 p19
[22] Bottou L, Bousquet O 2008 Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems Whistler, Canada, December 3−6, 2007 p161
[23] Mairal J, Bach F, Ponce J, Sapiro G 2009 Proceedings of the 26th Annual International Conference on Machine Learning Montreal, Canada, June 14–18, 2009 p689
[24] Flynn J, Neulander I, Philbin J, Snavely N 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, USA, June 27−30, 2016 p5515
目录
- 第69卷,第6期 - 2020年03月20日
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