光子学报, 2014, 43 (5): 0510002, 网络出版: 2014-06-03   

一种数字微镜阵列分区控制和超分辨重建的压缩感知成像法

A Method for Compressive Sensing of Images Based on Zone Control of Digital Micromirror Device and Super-resolution
作者单位
西安电子科技大学 综合业务网国家重点实验室,西安 710071
摘要
提出一种压缩感知成像框架结构.该结构采样端用新建的采样矩阵实现数字微镜阵列分区控制,可增强信息获取的准确性,测量得到与新数字微镜阵列对应的压缩采样值;重构端由采样值优化重构出低分辨率图像后,根据分区控制过程建立压缩感知理论框架下的超分辨重建模型,利用梯度稀疏约束优化算法进行求解,恢复出原高分辨率图像.实验结果表明: 数字微镜阵列分区控制与超分辨重建相结合的方法可以明显降低压缩感知成像系统的计算量,缩短成像时间,并且具有较高的图像重构质量.
Abstract
A compressive sensing based imaging framework was proposed. At the sampling end, in order to enhance the acquisition accuracy of information, a new measurement matrix was designed to carry out zone control on digital micromirror device and the new digital micromirror device with zone control was used to make compressive measurements. At the recovery end, a low resolution image was first recovered by solving an optimization problem from the received measurements. Then, the modeling of a super-resolution problem in a compressive sensing framework was constructed according to the zone control process, and a total variation algorithm was exploited to solve such a compressive sensing based super-resolution problem for the high resolution image. Experimental results show that the proposed method based on zone control of digital micromirror device and super-resolution reconstruction can greatly shorten the imaging time and has very low computational complexity and excellent recovery performance.
参考文献

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

[2] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

[3] CANDES E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematigue, 2008, 346(9-10): 589-592.

[4] CANDES E J, TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.

[5] SUN Xi-long, YU An-si, DONG Zhen, et al. Three-dimensional SAR focusing via compressive sensing: the case study of angel stadium[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 759-763.

[6] YANG Jun-gang, THOMPSON J, HUANG Xiao-tao, et al. Random-frequency SAR imaging based on compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 983-994.

[7] CHEN Jian-wen, CONG J, VESE L A, et al. A hybrid architecture for compressive sensing 3-D CT reconstruction[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012, 2(3): 616-625.

[8] LINGALA S G, JACOB M. Blind compressive sensing dynamic MRI[J]. IEEE Transactions on Medical Imaging, 2013, 32(6): 1132-1145.

[9] LI Cheng-bo, SUN Ting, KELLY K, et al. A compressive sensing and unmixing scheme for hyperspectral data processing[J]. IEEE Transactions on Image Processing, 2012, 21(3): 1200-1210.

[10] MA Jian-wei. Single-pixel remote sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 199-203.

[11] AUGUST Y, VACHMAN C, RIVENSON Y, et al. Stern. Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains[J]. Applied Optics, 2013, 52(10): D46-D54.

[12] TAKHAR D, LASKA J N, WAKIN M B, et al. A new compressive imaging camera architecture using optical-domain compression[C]. Proceedings of SPIE: Computational Imaging IV, San Jose, CA, 2006: 43-52.

[13] DUARTE M F, DAVENPOET M A, TAKHAR D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, [83-91], March 2008.

[14] CHAN Wai-lam, CHARAN K, TAKHAR D, et al. A single-pixel terahertz imaging systems based on compressed sensing[J]. Applied Physics Letters, 2008, 93(12): 121105-121105-3.

[15] CANDES E J, ROMBERG J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3): 969-986.

[16] WANG J, SHIM B. On recovery limit of orthogonal matching pursuit using restricted isometry property[J]. IEEE Transactions on Signal Processing, 2012, 60(9): 4973-4976.

[17] NEEDELL D, WARD R. Stable image reconstruction using total variation minimization[C/OL]. http://arxiv.org/pdf/1202.6429v9.pdf(2013.05.12).

[18] WU Xiao-lin, DONG Wei-sheng, ZHANG Xian-jun, et al. Model-assisted adaptive recovery of compressed sensing with imaging applications[J]. IEEE Transactions on Signal Processing, 2012, 21(2): 451-458.

[19] LIN Zhou-chen, HE Jun-feng, TANG Xiao-ou, et al. Limits of learning based superresolution algorithms[C]. Proceedings of ICCV, 2007: 1-8.

[20] YAND Jian-chao, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.

刘海英, 李云松, 吴成柯. 一种数字微镜阵列分区控制和超分辨重建的压缩感知成像法[J]. 光子学报, 2014, 43(5): 0510002. LIU Hai-ying, LI Yun-song, WU Cheng-ke. A Method for Compressive Sensing of Images Based on Zone Control of Digital Micromirror Device and Super-resolution[J]. ACTA PHOTONICA SINICA, 2014, 43(5): 0510002.

本文已被 5 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!