一种SLIP模型的图像增强新算法

doi:10.3969/j.issn.1001-2400.2014.01.019

J4 ›› 2014, Vol. 41 ›› Issue (1): 105-109+169.doi: 10.3969/j.issn.1001-2400.2014.01.019

一种SLIP模型的图像增强新算法

张广燕;王俊平;汪松;曹洪花;许丹;李锦;李长江

(西安电子科技大学通信工程学院，陕西西安 710071)

收稿日期:2013-05-06 出版日期:2014-02-20 发布日期:2014-04-02
作者简介:张广燕(1987-)，女，西安电子科技大学硕士研究生，E-mail：zh_gy_358@126.com．
基金资助:
国家自然科学基金资助项目(61173088)；西安市产业技术创新计划资助项目(CX1248⑤)

New image enhancement algorithm based on the SLIP model

ZHANG Guangyan;WANG Junping;WANG Song;CAO Honghua;XU Dan;LI Jin;LI Changjiang

(School of Telecommunication Engineering, Xidian Univ., Xi'an 710071， China)

Received:2013-05-06 Online:2014-02-20 Published:2014-04-02

摘要/Abstract

摘要：

提出了一种基于对称对数图像处理(SLIP)模型的图像增强新算法．该算法首先在分析现有对数图像增强算法的基础上，得到关于图像灰度值的SLIP图像增强算法，然后运用SLIP模型的向量运算，使SLIP图像增强算法简化为便于实现的简单数学运算．该算法既能以反射光图像的模式对图像进行增强，又能以透射光图像的模式对图像进行增强．通过主观评价和熵增强方法进行客观评价，结果表明，该算法不仅能增强图像的细节，还能提升图像的整体对比度，同时还克服了现有的对数图像增强算法不能以反射光图像的模式对图像进行增强的缺点．

关键词: 对数图像处理, 图像增强, 熵增强方法, 对比度

Abstract:

Image enhancement technology plays an important role in human life, social production, etc. This paper presents a new algorithm for image enhancement based on the SLIP (Symmetric Logarithmic Image Processing) model. Firstly, we get the SLIP image enhancement algorithm about image gray values based on the analysis of the existing logarithmic image enhancement algorithm. Then the SLIP image enhancement algorithm is simplified to the simple mathematical operation using the vector operation of the SLIP model. The proposed algorithm can enhance the image not only with the mode of the transmitted light image but also with the mode of the reflected light image. By means of subjective evaluation and EMEE objective evaluation, experimental results show that the proposed algorithm can enhance the image detail and improve the overall image contrast. Meanwhile, the algorithm overcomes the disadvantage that LIP image enhancement algorithm cannot enhance the images with the mode of the reflected light image.

Key words: logarithmic image processing(LIP), image enhancement, evaluation measure of enhancement by entropy(EMEE), contrast

中图分类号:

TP391.41

张广燕;王俊平;汪松;曹洪花;许丹;李锦;李长江. 一种SLIP模型的图像增强新算法[J]. J4, 2014, 41(1): 105-109+169.

ZHANG Guangyan;WANG Junping;WANG Song;CAO Honghua;XU Dan;LI Jin;LI Changjiang. New image enhancement algorithm based on the SLIP model[J]. J4, 2014, 41(1): 105-109+169.

参考文献

［1］李权合, 毕笃彦, 何林远. 退化过程模拟模型及其在图像增强中的应用［J］. 西安电子科技大学学报, 2011, 38(6): 185-192.
Li Quanhe, Bi Duyan, He Linyuan. Degradation Process Simulation Model and Its Use in Image Enhancement ［J］. Journal of Xidian University, 2011, 38(6): 185-192.
［2］ Jourlin M, Pinoli J. A Model for Logarithmic Image Processing ［J］. Journal of Microscopy, 1988, 149(1): 21-35.
［3］ Lee J S. Digital Image Enhancement and Noise Filtering by Use of Local Statistics ［J］. IEEE Transactionss on Pattern Analysis and Machine Intelligence, 1980, 2(2): 165-168.
［4］ Deng G. An Entropy Interpretation of the Logarithmic Image Processing Model with Application to Contrast Enhancement ［J］. IEEE Transactions on Image Processing, 2009, 18(5): 1135-1140.
［5］ Deng G. A Generalized Logarithmic Image Processing Model Based on the Giga-vision Sensor Model ［J］. IEEE Transactions on Image Processing, 2012, 21(3): 1406-1414.
［6］ Panetta K, Wharton E, Agaian S. Parameterization of Logarithmic Image Processing Models ［EB/OL］. ［2013-04-20］. http://citeseerx.ist.psu.edu/viewdoc/dowload?doi=10.1.1.152.4779&rep=rep1&type=pdf.
［7］ Panetta K A, Agaian S S, Zhou Y, et al. Parameterized Logarithmic Framework for Image Enhancement ［J］. IEEE Transactions on System, Man, and Cybernetics B, 2011, 41(2): 460-473.
［8］ Wharton E J, Panetta K, Agaian S S. Logarithmic Edge Detection with Applications ［C］//IEEE International Conference on Systems, Man, and Cybernetics. New York: IEEE, 2007: 3346-3351.
［9］ Nercessian S, Panetta K, Agaian S. Multiresolution Decomposition Schemes Using the Parameterized Logarithmic Image Processing Model with Application to Image Fusion ［DB/OL］. ［2012-12-20］. http://dl.acm.org/citation.cfm?id=1945447.
［10］ Vorobel R, Botsian V. Parameterization Method for Logarithmic Image Processing Model ［C］//Modern Problems of Radio Engineering Telecommunications and Computer Science. Piscataway: IEEE Computer Society, 2012: 408.
［11］ Patrascu V. Rough Sets on Four-Valued Fuzzy Approximation Space ［C］//IEEE International Conference on Fuzzy Systems. Piscataway: IEEE, 2007: 217-221.
［12］ Vertan C, Oprea A, Florea C, et al. A Pseudo-logarithmic Image Processing Framework for Edge Detection ［C］//Advanced Concepts for Intelligent Vision Systems. Heidelberg: Springer Verlag, 2008: 637-644.
［13］ Florea C, Vertan C. Piecewise Linear Approximation of Logarithmic Image Processing Models for Dynamic Range Enhancement ［J］. UPB Scientific Bulletin, Series C: Electrical Engineering, 2009, 71(2): 3-14.
［14］ Navarro L, Courbebaisse G. Symmetric Logarithmic Image Processing Model, Application to Laplacian Edge Detection ［DB/OL］. ［2012-06-10］. http://hal-emse.ccsd.cnrs.fr/hal-00709350/.
［15］ Yu Tianhe, Li Qiuming, Dai Jingmin. New Enhancement of Infrared Image Based on Human Visual System ［J］. China Optical Letters, 2009, 7(1): 206-209.
［16］崔力, 陈玉坤, 韩宇. 视觉相似性图像质量评价方法［J］. 西安电子科技大学学报, 2012, 39(4): 155-160.
Cui Li, Chen Yukun, Han Yu. Visual Similarity Index for Image Quality Assessment ［J］. Journal of Xidian University, 2012, 39(4): 155-160.
［17］ Panetta K, Wharton E J, Agaian S S. Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure ［J］. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 2008, 38(1): 174-188.
［18］ Wharton E, Panetta K, Agaian S. Human Visual System Based Similarity Metrics ［C］//Proceedings of IEEE International Conference on Systems, Man and Cybernetics. New York: IEEE, 2008: 685-690.

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一种SLIP模型的图像增强新算法

New image enhancement algorithm based on the SLIP model

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