Compressed Imaging with Frequency-Domain Phase Modulation When in 4-f Optical Architecture

doi:10.13190/j.jbupt.2014.03.003

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2014, Vol. 37 ›› Issue (3): 13-17.doi: 10.13190/j.jbupt.2014.03.003

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Compressed Imaging with Frequency-Domain Phase Modulation When in 4-f Optical Architecture

ZHANG Cheng¹, CHENG Hong^1,2, ZHANG Fen¹, WEI Sui¹

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

Received:2013-11-15 Online:2014-06-28 Published:2014-06-08

Abstract

Abstract:

One of the goals of optical imaging and image processing is super-resolution imaging. In order to reduce the registration error and costly problem facing in multiple sub-pixel image registration fusion method to achieve super-resolution, a compressive sensing method is introduced for super-resolution imaging, it benefits from the general sparse representation feature of most nature images. Based on the classical 4-f optical architecture, the phase will contain more information than the amplitude in frequency domain, a compressed imaging method with pure phase modulation in the frequency domain is proposed. The original high-resolution image information can be recovered from the low-dimensional measurements recorded with a single exposure by various algorithms. Numerical results demonstrate that the proposed can effectively achieve random modulation of image information and high-quality reconstruction, which can be considered as a promising scheme for physics implementation of compressed imaging with high reconstruction signal to noise ratio and less reconstruction time, especially for large-scale image.

Key words: compressed imaging, image super-resolution, frequency phase modulation, 4-f optical architecture

CLC Number:

TN911.7

ZHANG Cheng, CHENG Hong, ZHANG Fen, WEI Sui. Compressed Imaging with Frequency-Domain Phase Modulation When in 4-f Optical Architecture[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(3): 13-17.

References

[1] Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Transactions on Signal Processing Magazine, 2008, 25(2): 83-91.

[2] Stern A, Javidi B. Random projections imaging with extended space-bandwidth product[J]. Journal of Display Technology, 2007, 3(3): 315-320.

[3] 张成,杨海蓉,韦穗. 循环-托普利兹块相位掩模可压缩双透镜成像[J]. 光学学报,2011, 31(8):106-111. Zhang Cheng, Yang Hairong, Wei Sui. Compressive double-lens imaging using circulant-toeplitz-block phase mask[J]. Acta Optica Sinica, 2011, 31(8): 106-111.

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

[5] Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.

[6] Tropp J A, Laska J N, Duarte M F, et al. Beyond nyquist: efficient sampling of sparse bandlimited signals[J]. IEEE Transactions on Information Theory, 2010, 56(1): 520-544.

[7] Romberg J. Compressive sensing by random convolution[J]. SIAM Journal on Imaging Sciences, 2009, 2(4): 1098-1128.

[8] Bioucas D J M, Figueiredo M. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12): 2992-3004.

[9] Figueiredo M, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.

[10] Yin Wotao, Morgan S, Yang Junfeng, et al. Practical compressive sensing with toeplitz and circulant matrices[EB/OL]. Visual Communications and Image Processing 2010. International Society for Optics and Photonics, 2010[2012-10-21]. http://spie. org/Publications/Proceedings/Paper/10. 1117/12. 863527.

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Compressed Imaging with Frequency-Domain Phase Modulation When in 4-f Optical Architecture

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