Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition

Ya-li QIN; Xiao-shuai ZHANG; Lin-qian YU

doi:10.3788/OPE.20172505.1171

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Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition

Modern Applied Optics | 更新时间：2020-07-07

- Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition
- Optics and Precision Engineering Vol. 25, Issue 5, Pages: 1171-1177(2017)
- 作者机构：
  
  浙江工业大学信息工程学院, 浙江杭州 310023
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172505.1171
  CLC： TN911.74;TP391.41
- Received：21 July 2016，
  
  Accepted：16 August 2016，
  
  Published：25 May 2017
- 稿件说明：
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Ya-li QIN, Xiao-shuai ZHANG, Lin-qian YU. Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition[J]. Optics and precision engineering, 2017, 25(5): 1171-1177.
DOI：

Ya-li QIN, Xiao-shuai ZHANG, Lin-qian YU. Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition[J]. Optics and precision engineering, 2017, 25(5): 1171-1177. DOI： 10.3788/OPE.20172505.1171.

摘要

光场成像技术中光场的采集和数据的压缩处理是亟待解决的问题。为了实现光场的稀疏采样和恢复，建立了基于光场低秩结构的压缩采样相机系统，研究了光场矩阵的结构特征及压缩采样下光场图像的重构问题。根据静态光场各视点图像之间的内容相似性，将这些图像向量化并按列组合成一个二维矩阵，该矩阵呈现出低秩或近似低秩的状态。对光场图像矩阵进行低秩分解，结果表明偏离低秩的部分呈现出很强的稀疏性性质，低秩和稀疏各自表征不同的数据冗余度。然后，对基于掩膜的相机采样系统进行随机Noiselets变换测量，鉴于重构过程是一个低秩稀疏相关性约束下的优化求解问题，采用贪婪迭代求解分别重构出光场矩阵的低秩部分和稀疏部分。仿真结果表明，重构图像的PSNR维持在25 dB以上，且保留了光场视点间的视差信息，能够满足稀疏采样中对光场图像的要求。

Abstract

Collection of light field and compression of data in light field imaging technology are urgent problems which need to be solved. In order to realize sparse sampling and restoration of the light field

a camera system to compress samplings based on low-rank structure of the light field was built for researching structural features of matrix of the light field and the reconstruction of light field images under compressive sampling. According to content similarities between each viewpoint image in static light field

those images were vectorized into a two-dimensional matrix by columns. The matrix presented a low-rank or approximated low-rank state. Low-rank decomposition of image matrix in the light field were finished

which shows that deflective low-rank parts emerge strong sparse properties

and low-rank and sparseness separately represented different data redundancies. Then

the camera sampling system fitted with the mask was measured through sparse random Noiselets conversion. Considering the reconstruction process was an optimization solution problem constrained by low-rank sparse correlation

the greedy iterative solution was adopted to separately reconstruct low-rank parts and sparse parts of light field matrix. The simulation result shows that the PSNR of reconstructed image that keeps disparity information among viewpoints of the light field maintains over 25 dB

thus meeting the requirement of sparse sampling for images of light field.

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references

LEVOY M. Light fields and computational imaging[J]. Computer , 2006, 39(8):46-55.

CANDE E J, WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine , 2008, 25(2):21-30.

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.

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.

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.

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.

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.

COIFMAN R, GESHWIND F, MEYER Y. Noiselets[J]. Applied and Computational Harmonic Analysis , 2001, 10(1):27-44.

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.

刘永春, 龚华军, 沈春林.基于掩膜的光场采集与重建的研究[J].光学学报, 2014, 34(8):0810001.

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)

OTAZO R, CANDÈS 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.

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.

马坚伟, 徐杰, 鲍跃全, 等.压缩感知及其应用:从稀疏约束到低秩约束优化[J].信号处理, 2012, 28(5):609-623.

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)

沈燕飞, 朱珍民, 张勇东, 等.基于秩极小化的压缩感知图像恢复算法[J].电子学报, 2016, 44(3):572-579.

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)

敬朝阳, 杨晓梅, 王郗雨.基于稀疏与低秩的核磁共振图像重构算法[J].计算机应用研究, 2015, 32(3):942-945.

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)

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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