Computer Science ›› 2017, Vol. 44 ›› Issue (11): 301-304.doi: 10.11896/j.issn.1002-137X.2017.11.046

Previous Articles     Next Articles

Research of Document Image Super Resolution Algorithm Based on Directional Bilateral Total Variation Regularization

LIU Xiao-yuan, YI Yang, YANG Lei and WANG Bin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: For super resolution image reconstruction problem in the traditional document,based on bilateral total variation regularization super-resolution algorithm,a document image super resolution algorithm based on improved BTV was proposed.By introducing a new regularization term,which is the direction of the stroke width,the algorithm is adaptive to the local width and the local direction of the character strokes.Then,the proposed algorithm can make the local stroke width of the output image close to the local stroke direction by analyzing the low resolution image and its interpolation.This information is compressed into the directional total variation regularization term based on the width of stroke.By linear combination of minimization regularization and data fidelity term,the high-resolution images are reconstructed.Compared with the related document image super resolution method,the proposed method has been improved in the visual image quality and character recognition accuracy.

Key words: Document image,Super resolution,Image enhancement,Bilateral total variation,Regular terms

[1] REN F Q,QIU T S,HAN J,et al.Multi frame image superresolution reconstruction based on two order generalized total variation[J].Journal of Electronic Science,2015,43(7):1275-1280.(in Chinese) 任福全,邱天爽,韩军,等.基于二阶广义全变差的多帧图像超分辨率重建[J].电子学报,2015,43(7):1275-1280.
[2] HE J X,REN H P,ZENG Q Y,et al.Super resolution recon-struction of weather radar echo based on improved total variation[J].Computer Simulation,2014,31(7):415-418.(in Chinese) 何建新,任红萍,曾强宇,等.基于改进全变差的天气雷达回波超分辨率重建[J].计算机仿真,2014,31(7):415-418.
[3] ZHANG L,LIU Z M,TANG J.The realization of handwritten numeral recognition method based on BP neural network [J].Automation and Instrumentation,2015(6):169-170.(in Chinese) 张黎,刘争鸣,唐军.基于BP神经网络的手写数字识别方法的实现[J].自动化与仪器仪表,2015(6):169-170.
[4] LI C F,YANG X,ZHANG X M,et al.Research on ultrasonic image denoising algorithm based on [J].MAP Journal of Electronic Science,2014(7):1291-1298.(in Chinese) 李春芳,杨鑫,张旭明,等.基于MAP的超声图像分解去噪算法研究[J].电子学报,2014(7):1291-1298.
[5] YANG S F,ZHAO R Z.Research and development of image super resolution reconstruction based on low rank matrix and dictionary learning [J].Computer Research and Development,2016,53(4):884-891.(in Chinese) 杨帅锋,赵瑞珍.基于低秩矩阵和字典学习的图像超分辨率重建[J].计算机研究与发展,2016,53(4):884-891.
[6] ABEDI A,KABIR E.Stroke width-based directional total variation regularisation for document image super resolution[J].Iet Image Processing,2015,10(2):158-166.
[7] AYUBI S D,BAJWA U I,ANWAR M W.Super-ResolutionBased Enhancement of Cardiac MR Images[J].Current Medical Imaging Reviews,2015,11(999):1.
[8] SHI F,CHENG J,WANG L,et al.LRTV:MR Image Super- Resolution With Low-Rank and Total Variation Regularizations[J].IEEE Transactions on Medical Imaging,2015,34(12):2459-2466.
[9] RUDIN L,OSHER S,FATEMI E.Nonlinear total variationbased noise removel algorithms[J].Physical D Nonlinear Phenomena,1992,60(1-4):259-268.
[10] FARSIU S,ROBINSON D.Fast and Robust Multiframe SuperResolution[J].IEEE Transactions on Image Processing,2004,13(10):1327-1344.
[11] JIANG J,MA X,CAI Z,et al.Sparse Support Regression forImage Super-Resolution[J].IEEE Photonics Journal,2015,7(5):1-11.
[12] CHU N,MOHAMMAD-DJAFARI A,PICHERAL J.RobustBayesian super-resolution approach via sparsity enforcing a priori for near-field aeroacoustic source imaging[J].Journal of Sound & Vibration,2013,2(18):4369-4389.
[13] XIE J,FERIS R S,YU S S,et al.Joint Super Resolution andDenoising from a Single Depth Image[J].IEEE Transactions on Multimedia,2015,17(9):1525-1537.
[14] SURYANARAYANA G,DHULI R.Simultaneous edge preserving and noise mitigating image super-resolution algorithm[J].AEU-International Journal of Electronics and Communications,2016,70(4):409-415.
[15] XIANG H Y.Review of Super-resolution Image RestorationMethod[J].Journal of Chongqing University of Technology(Natural Science),2014,8(9):72-76.(in Chinese) 向海燕.超分辨率图像恢复方法综述[J].重庆理工大学学报(自然科学版),2014,28(9):72-76.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!