基于区域对比度增强的二值化算法

卢迪; 黄鑫; 柳长源; 林雪; 张华玉; 严俊

doi:10.11999/JEIT160197

基于区域对比度增强的二值化算法

doi: 10.11999/JEIT160197

卢迪¹,
黄鑫^1, ,,
柳长源¹,
林雪¹,
张华玉¹,
严俊¹

基金项目:

哈尔滨市科技创新人才项目(2014RFQXJ163)

计量
- 文章访问数: 1557
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- 被引次数: 0
出版历程
- 收稿日期: 2016-03-03
- 修回日期: 2016-07-12
- 刊出日期: 2017-01-19

Binarization Method Based on Local Contrast Enhancement

Funds:

The Science and Technology Innovation Talents Project of Harbin (2014RFQXJ163)

摘要

摘要: 降质文档图像二值化问题是图像处理领域的一个难点。该文通过分析图像不同区域灰度对比度的差异，为降质文档图像提出了新的二值化算法。首先利用四叉树原理自适应划分区域，再对不同灰度对比度区域采用不同对比度增强法以调整局部区域内的灰度对比度，最后根据灰度值出现的频率确定局部阈值。该文测试了随机拍摄的降质图像及DIBCO(Document Image Binarization COntest)图像集中的50幅图像。与4种经典算法比较，所提算法处理的降质图像具有最高F-measure值和峰值信噪比(PSNR值)。
- 图像处理 /
- 二值化 /
- 区域对比度增强 /
- 局部阈值 /
- 四叉树法
Abstract: Binarization for degraded document images is a difficult point in image processing. This paper presents a new binarization method for the degraded document images by analyzing the differences of image grayscale contrast in different areas. Firstly, theory of quadtree is used to divide areas adaptively. Secondly, various contrast enhancements are selected to adjust local grayscale contrast for different contrast areas. Lastly, the frequency of gray value is utilized to calculate threshold. The proposed algorithm is tested on random shooting degraded images and datasets of Document Image Binarization COntest (DIBCO). Compared with other four classical algorithms, the binaried images using the proposed algorithm gain the highest F-measure and PSNR (Peak Signal-to-Noise Ratio).
- Image processing /
- Binarization /
- Local contrast enhancement /
- Local threshold /
- Quadtree

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参考文献(13)

OTSU N. A threshold selection method from gray level histograms[J]. IEEE Transactions on Systems, Man and Cybernetics, 1979, 9(1): 62-66. doi: 10.1109/TSMC.1979. 4310076.

申铉京, 龙建武, 陈海鹏, 等. 三维直方图重建和降维的Otsu阈值分割算法[J]. 电子学报, 2011, 39(5): 1108-1114.

SHEN Xuanjing, LONG Jianwu, CHEN Haipeng, et al. Otsu thresholding algorithm based on rebuilding and dimension reduction of the 3-dimensional histogram[J]. Acta Electronica Sinica, 2011, 39(5): 1108-1114.

NIBLACK W. An Introduction to Digital Image Processing [M]. Englewood Cliffs, NJ, US, Prentice-Hall, Inc., 1986: 115-116.

SAUVOLA J and PIETIKAINEN M. Adaptive document image binarization[J]. Pattern Recognit, 2000, 33(2): 225-236. doi: 10.1016/S0031-3203(99)00055-2.

MA L and STAUNTON R C. A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns[J]. Pattern Recognition, 2007, 40(11): 3005-3011. doi: 10.1016/j.patcog.2007.02.005.

CHOU C H, and LIN W H, and CHANG F. A binarization method with learning-build rules for document images produced by cameras[J]. Pattern Recognition, 2010, 43(4): 1518-1530. doi: 10.1016/j.patcog.2009.10.016.

龙建武, 申铉京, 臧慧, 等. 高斯尺度空间下估计背景的自适应阈值分割算法[J]. 自动化学报, 2014, 40(8): 1773-1782. doi: 10.3724/SP.J.1004.2014.01773.

LONG Jianwu, SHEN Xuanjing, ZANG Hui, et al. An adaptive thresholding algorithm by background estimation in Gaussian scale space[J]. Acta Automatica Sinica, 2014, 40(8): 1773-1782. doi: 10.3724/SP.J.1004.2014.01773.

SINGH B M, SHARMA R, GHOSH D, et al. Adaptive binarization of severely degraded and non-uniformly illuminated documents[J]. International Journal of Document Analysis and Recognition, 2014, 17(4): 393-412. doi: 10.1007/ s10032-014-0219-6.

MESQUITA R G, MELLO C A B, and ALMEIDA L H E V. A new thresholding algorithm for document images based on the perception of objects by distance[J]. Integrated Computer-Aided Engineering, 2014, 21(2): 133-146. doi: 10.3233/ICA-130453.

MILYAEV S, BARINOVA O, NOVIKOVA T, et al. Fast and accurate scene text understanding with image binarization and off-the-shelf OCR[J]. International Journal of Document Analysis and Recognition, 2015, 18(2): 169-182. doi: 10.1007/ s10032-015-0240-4.

ROSENFELD A and KAK A C. Digital Picture Processing [M]. 2nd ed. New York, Morgan Kaufmann: Academic Press, 1982: 92-95.

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