Optics and Precision Engineering, Volume. 25, Issue 5, 1171(2017)
Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition
[1] [1] LEVOY M. Light fields and computational imaging [J]. Computer, 2006, 39(8): 46-55.
[2] [2] CANDE E J, WAKIN M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
[3] [3] MARWAH K, WETZSTEIN G, BANDO Y, et al.. Compressive light field photography using overcomplete dictionaries and optimized projections [J]. ACM Transactions on Graphics, 2013, 32(4): 46.
[4] [4] 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.
[5] [5] KAMAL M H, GOLBABAEE M, VANDERGHEYNST P. Light field compressive sensing in camera arrays [C]. Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2012: 5413-5416.
[6] [6] KAMAL M H, VANDERGHEYNST P. Joint low-rank and sparse light field modelling for dense multiview data compression [C].Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2013: 3831-3835.
[7] [7] VEERARAGHAVAN A, RASKAR R, AGRAWAL A, et al.. Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing [J]. ACM Transactions on Graphics, 2007, 26(3): 69.
[8] [8] COIFMAN R, GESHWIND F, MEYER Y. Noiselets [J].Applied and Computational Harmonic Analysis, 2001, 10(1): 27-44.
[9] [9] WATERS A E, SANKARANARAYANAN A C, BARANIUK R G. SpaRCS: Recovering low-rank and sparse matrices from compressive measurements [C].Advances in Neural Information Processing Systems 24, NIPS, 2011: 1089-1097.
[10] [10] LIU Y CH, GONG H J, CHEN CH L.Research of light field acquisition and reconstruction based on mask [J]. Acta Optica Sinica, 2014, 34(8): 0810001.(in Chinese)
[11] [11] OTAZO R, CANDS E, SODICKSON D K. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components [J]. Magnetic Resonance in Medicine Official Journal of the Society of Magnetic Resonance in Medicine, 2015, 73(3): 1125-1136.
[12] [12] KAMAL M H, HESHMAT B, RASKAR R, et al.. Tensor low-rank and sparse light field photography [J]. Computer Vision and Image Understanding, 2016, 145: 172-181.
[13] [13] MA J W, XU J, BAO Y Q, et al.. Compressive sensing and its application: from sparse to low-rank regularized optimization [J]. Journal of Signal Processing, 2012, 28(5): 609-623. (in Chinese)
[14] [14] SHEN Y F, ZHU ZH M, ZHANG Y D, et al.. Compressed sensing image reconstruction algorithm based on rank minimization [J].Acta Electronica Sinica, 2016, 44(3): 572-579. (in Chinese)
[15] [15] JING ZH Y, YANG X M, WANG X Y.Low-rank and sparsity-based MRI reconstruction algorithm [J]. Application Research of Computers, 2015, 32(3): 942-945. (in Chinese)
[16] [16] WANNER S, MEISTER S, GOLDLUECKE B. Datasets and Benchmarks for Densely Sampled 4D Light Fields [M]//BRONSTEIN M, FAVRE J, HORMANN K. Vision, Modeling, and Visualization. The Eurographics Association, 2013.
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QIN Ya-li, ZHANG Xiao-shuai, YU Lin-qian. Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition[J]. Optics and Precision Engineering, 2017, 25(5): 1171
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Received: Jul. 21, 2016
Accepted: --
Published Online: Jun. 30, 2017
The Author Email: Ya-li QIN (ylqin@zjut.edu.cn)