[1] Lu Xiaobo, Zeng Weili,. Super-resolution reconstructionfor license plate images of moving vehicles [J]. Journal of Southeast University (English Edition), 2010, 26 (3): 457-460. [doi:10.3969/j.issn.1003-7985.2010.03.017]

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Super-resolution reconstructionfor license plate images of moving vehicles()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

26

Issue:

2010 3

Page:

457-460

Research Field:

Computer Science and Engineering

Publishing date:

2010-09-30

Info

Title:

Super-resolution reconstructionfor license plate images of moving vehicles

Author(s):

Lu Xiaobo¹;  Zeng Weili²

¹ School of Automation, Southeast University, Nanjing 210096, China
² School of Transportation, Southeast University, Nanjing 210096, China

Keywords:

super-resolution;  residual gradient term;  residual data term;  license plate;  regularization

PACS:

TP391.41

DOI:

10.3969/j.issn.1003-7985.2010.03.017

Abstract:

A novel reconstruction method to improve the recognition of license plate texts of moving vehicles in real traffic videos is proposed, which fuses complimentary information among low resolution(LR)images to yield a high resolution(HR)image. Based on the regularization super-resolution(SR)reconstruction schemes, this paper first introduces a residual gradient(RG)term as a new regularization term to improve the quality of the reconstructed image. Moreover, L₁ norm is used to measure the residual data(RD)term and the RG term in order to improve the robustness of the proposed method. Finally, the steepest descent method is exploited to solve the energy functional. Simulated and real acquired video sequence experiments show the effectiveness and practicability of the proposed method and demonstrate its superiority over the bi-cubic interpolation and discontinuity adaptive Markov random field(DAMRF)SR method in both signal to noise ratios(SNR)and visual effects.

References:

[1] Anagnostopoulos C N E, Anagnostopoulos I E, Psoroulas I D, et al. License plate recognition from still images and video sequences: a survey [J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(3): 377-391.
[2] Takeda H, Milanfar P, Protter M, et al. Super-resolution without explicit subpixel motion estimation [J]. IEEE Transactions on Image Processing, 2009, 18(9): 1958-1975.
[3] Baker S, Kanade T. Limits on super-resolution and how to break them [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(9): 1167-1183.
[4] Farsiu S, Robinson D, Elad M, et al. Fast and robust multi-frame super-resolution [J]. IEEE Transactions on Image Processing, 2004, 3(10): 1327-1344.
[5] Farsiu S, Robinson D, Elad M, et al. Advances and challenges in super-resolution [J]. International Journal of Imaging Systems and Technology, 2004, 14(2): 47-57.
[6] Park S C, Park M K, Kang M G.. Super-resolution image reconstruction: a technical review[J]. IEEE Signal Processing Magazine, 2003, 1(5): 21-36.
[7] Huang T S, Tsai R Y. Multi-frame image restoration and registration [J]. Advances in Computer Vision and Image Processing, 1984, 1(2): 317-339.
[8] Chaudhuri S, Taur D R. High-resolution slow-motion sequencing—how to generate a slow-motion sequence from a bit stream [J]. IEEE Signal Processing Magazine, 2005, 22(2):16-24.
[9] Matan P, Elad M. Super resolution with probabilistic motion estimation [J]. IEEE Transactions on Image Processing, 2009, 18(8): 1899-1904.
[10] Suresh K V, Kumar G M, Rajagopalan H N. Super-resolution of license plates in real traffic videos [J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(2): 321-331.

Memo

Memo:

Biography: Lu Xiaobo(1965—), male, doctor, professor, xblu@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.60972001), the National Key Technology R& D Program of China during the 11th Five-Year Plan Period(No.2009BAG13A06).
Citation: Lu Xiaobo, Zeng Weili.Super-resolution reconstruction for license plate images of moving vehicles[J].Journal of Southeast University(English Edition), 2010, 26(3):457-460.

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