彩色全息压缩重构

张成; 沈川; 程鸿; 章权兵; 陈岚; 韦穗

doi:10.16383/j.aas.2015.c131140

彩色全息压缩重构

doi: 10.16383/j.aas.2015.c131140

张成^1,2,
沈川¹,
程鸿¹,
章权兵¹,
陈岚¹,
韦穗^1, ,

1.
安徽大学计算智能与信号处理教育部重点实验室合肥 230039;
2.
安徽省现代成像与显示技术重点实验室合肥 230039

基金项目:

国家自然科学基金(61201396,61201227,61301296,61377006),国家自然科学基金--广东联合基金(U1201255),安徽省自然科学基金(1208085QF114)和安徽大学博士科研启动经费(33190218)资助

详细信息

作者简介:
张成安徽大学电子信息工程学院讲师. 2012 年获安徽大学电子信息工程学院博士学位. 主要研究方向为压缩感知,矩阵填充, 光学成像和相位恢复.E-mail: question1996@163.com

通讯作者:
韦穗安徽大学教授. 主要研究方向为计算机视觉, 图像处理, 全息显示. 本文通信作者. E-mail: swei@ahu.edu.cn

计量
- 文章访问数: 1900
- HTML全文浏览量: 96
- PDF下载量: 1503
- 被引次数: 0
出版历程
- 收稿日期: 2013-12-12
- 修回日期: 2014-10-12
- 刊出日期: 2015-02-20

Compressed Reconstruction of Color Holography

1.
Key Laboratory of Intelligent and Computing Signal Processing, Anhui University, Hefei 230039;
2.
Key Laboratory of Modern Imaging and Displaying Technology of Anhui Province, Hefei 230039

Funds:

Supported by National Natural Science Foundation of China (61201396, 61201227, 61301296, 61377006), National Natural Science Foundation of China--Guangdong Joint Fund (U1201255), Natural Science Foundation of Anhui Province (1208085QF114), and Starting Research Fund of Anhui University (33190218)

摘要

摘要: 压缩全息搭起Gabor全息和压缩感知(Compressed sensing, CS)理论之间的桥梁, 特别适合从单帧二维全息测量数据中重建三维对象, 是一种新兴的三维重建技术. 本文将压缩全息方法从单波长情形推广到多波长, 提出一种基于三维总变分稀疏模型的改进彩色全息压缩成像方法, 建立多波长情形下的压缩测量模型. 该方法利用对象的稀疏先验知识, 从单帧二维彩色全息图中重建多波长三维对象, 有效地实现孪生像的抑制和多层切片相互之间的散焦图像对重建质量的影响. 数值实验结果验证了本文提出方法的有效性.
- 压缩感知 /
- 压缩全息 /
- 三维重建 /
- 彩色全息
Abstract: Compressed holography is an emerging 3D reconstruction technique, which bridges the gap between compressed sensing (CS) theory and Gabor's holography, especially for rebuilding 3D objects from a single-frame 2D holography measurement data. In this paper, the single-wavelength settings in compressed holography are extended to the multi-wavelength, and an improved compressed color holography imaging method is proposed, and a compressed measurement model in multi-wavelength case is established. Utilizing sparse prior knowledge of an object, a multi-wavelength 3D object can be reconstructed effectively from single-frame 2D color holography data of the object, so as to suppress the twin image and the defocus image due to multilayer slices and thus improve high quality reconstruction. Numerical results have demonstrated the effectiveness of our method.
- Compressed sensing (CS) /
- compressed holography /
- 3D reconstruction /
- color holography

HTML全文

参考文献(32)

[1]	Schnars U, Jueptner W. Digital Holography. Berlin, Heidelberg: Springer, 2005
[2]	[2] Tziraki M, Jones R, French P M W, Melloch M R, Nolte D D. Photorefractive holography for imaging through turbid media using low coherence light. Applied Physics B, 2000, 70(1): 151-154
[3]	[3] Cands E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223
[4]	[4] Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306
[5]	Liu Fang, Wu Jiao, Yang Shu-Yuan, Jiao Li-Cheng. Research advances on structured compressive sensing. Acta Automatica Sinica, 2013, 39(12): 1980-1995(刘芳, 武娇, 杨淑媛, 焦李成. 结构化压缩感知研究进展. 自动化学报, 2013, 39(12): 1980-1995)
[6]	Peng Yi-Gang, Suo Jin-Li, Dai Qiong-Hai, Xu Wen-Li. From compressed sensing to low-rank matrix recovery: theory and applications. Acta Automatica Sinica, 2013, 39(7): 981-994(彭义刚, 索津莉, 戴琼海, 徐文立. 从压缩传感到低秩矩阵恢复: 理论与应用. 自动化学报, 2013, 39(7): 981-994)
[7]	Ren Yue-Mei, Zhang Yan-Ning, Li Ying. Advances and perspective on compressed sensing and application on image processing. Acta Automatica Sinica, 2014, 40(8): 1563-1575(任越美, 张艳宁, 李映. 压缩感知及其图像处理应用研究进展与展望. 自动化学报, 2014, 40(8): 1563-1575)
[8]	[8] Denis L, Lorenz D, Thibaut E, Fournier C, Trede D. Inline hologram reconstruction with sparsity constraints. Optics Letters, 2009, 34(22): 3475-3477
[9]	[9] Brady D J, Choi K, Marks D L, Horisaki R, Lim S. Compressive holography. Optics Express, 2009, 17(15): 13040-13049
[10]	Gabor D. A new microscopic principle. Nature, 1948, 161(4098): 777-778
[11]	Hahn J, Lim S, Choi K, Horisaki R, Brady D J. Video-rate compressive holographic microscopic tomography. Optics Express, 2011, 19(8): 7289-7298
[12]	Cull C F, Wikner D A, Mait J N, Mattheiss M, Brady D J. Millimeter-wave compressive holography. Applied Optics, 2010, 49(19): E67-E82
[13]	Choi K, Horisaki R, Hahn J, Lim S, Marks D L, Schulz T J, Brady D J. Compressive holography of diffuse objects. Applied Optics, 2010, 49(34): H1-H10
[14]	Gehm M E, John R, Brady D J, Willett R M, Schulz T J. Single-shot compressive spectral imaging with a dual-disperser architecture. Optics Express, 2007, 15(21): 14013-14027
[15]	Wagadarikar A, John R, Willett R, Brady D J. Single disperser design for coded aperture snapshot spectral imaging. Applied Optics, 2008, 47(10): B44-B51
[16]	Stern A, Rivenson Y. Theoretical bounds on Fresnel compressive holography performance. Chinese Optics Letters, 2014, 12(6): 060022, DOI: 10.3788/COL201412.060022
[17]	Rivenson Y, Stern A. Conditions for practicing compressive Fresnel holography. Optics Letters, 2011, 36(17): 3365-3367
[18]	Horisaki R, Tanida J, Stern A, Javidi B. Multidimensional imaging using compressive Fresnel holography. Optics Letters, 2012, 37(11): 2013-2015
[19]	Horisaki R, Xiao X, Tanida J, Javidi B. Feasibility study for compressive multi-dimensional integral imaging. Optics Express, 2013, 21(4): 4263-4279
[20]	Rivenson Y, Stern A, Rosen J. Reconstruction guarantees for compressive tomographic holography. Optics Letters, 2013, 38(14): 2509-2511
[21]	Rivenson Y, Rot A, Balber S, Stern A, Rosen J. Recovery of partially occluded objects by applying compressive Fresnel holography. Optics Letters, 2012, 37(10): 1757-1759
[22]	Rivenson Y, Stern A, Rosen J. Compressive multiple view projection incoherent holography. Optics Express, 2011, 19(7): 6109-6118
[23]	Wu Ying-Chun, Wu Xue-Cheng, Wang Zhi-Hua, Chen Ling-Hong, Zhou Hao, Cen Ke-Fa. Reconstruction of digital inline hologram with compressed sensing. Acta Optica Sinica, 2011, 31(11): 1109001-1-1109001-6(吴迎春, 吴学成, 王智化, 陈玲红, 周昊, 岑可法. 压缩感知重建数字同轴全息. 光学学报, 2011, 31(11): 1109001-1-1109001-6)
[24]	Pei Hui, Yang Zhen-Ya, Zheng Chu-Jun. Phase-shifting on-axis Fourier transform digital holography based on compressed sensing. Laser Optoelectronics Progress, 2013, 50(4): 040901(裴慧, 杨振亚, 郑楚君. 基于压缩传感的相移同轴傅里叶变换数字全息. 光学学报, 2013, 50(4): 040901)
[25]	Han Chao, Wu Wei, Li Meng-Meng. Encoding and reconstruction of lensless off-axis Fourier hologram based on the theory of compressed sensing. Chinese Journal of Lasers, 2014, 41(2): 0209015-1-0209015-5(韩超, 吴伟, 李蒙蒙. 基于压缩感知理论的无透镜离轴傅里叶全息编码与重建. 中国激光, 2014, 41(2): 0209015-1-0209015-5)
[26]	Goodman J W. Statistical Optics. New York: Wiley Inter-Science, 2009
[27]	Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 1992, 60(1-4): 259-268
[28]	Le Montagner Y, Angelini E, Olivo-Marin J C. Video reconstruction using compressed sensing measurements and 3D total variation regularization for bio-imaging applications. In: Proceedings of the 19th IEEE International Conference on Image Processing. London: IEEE, 2012. 917-920
[29]	Bioucas-Dias J M, Figueiredo M A T. A new twist: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Transactions on Image Processing, 2007, 16(12): 2992-3004
[30]	Chambolle A. An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision, 2004, 20(1-2): 89-97
[31]	van den Berg E, Friedlander M P. Probing the Pareto frontier for basis pursuit solutions. SIAM Journal on Scientific Computing, 2008, 31(2): 890-912
[32]	van den Berg E, Friedlander M P. Sparse optimization with least-squares constraints. SIAM Journal on Optimization, 2011, 21(4): 1201-1229