中国激光, 2014, 41 (3): 0309002, 网络出版: 2014-03-03   

双峰高斯函数规定化的变分红外图像增强

Variable Infrared Image Enhancement of Bimodal Gaussian Function Specification
作者单位
1 中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033
2 中国科学院大学, 北京 100049
摘要
针对红外图像具有目标边缘和细节模糊的缺点,提出一种双峰高斯函数规定化的变分红外图像增强算法。该方法将图像变换到梯度域,得到图像的梯度直方图,构造出一个双峰高斯函数,以此对梯度直方图的分布加以约束,用变分方法对梯度场中增强的图像进行重建以提高图像的对比度和增强目标边缘和细节信息。为了防止噪声被放大,在构造增强的梯度场时,对噪声做了幅值切割。实验结果表明,该算法无论是在视觉效果上还是在图像信息熵评估值定量指标上均明显优于直方图均衡化和规定化算法,为红外图像提供了很好的视觉效果。
Abstract
Infrared images typically have the problems of target fuzzy edges and details. The variable infrared image enhancement algorithm of bimodal Gaussian function specification is introduced. Firstly, by converting the image to the gradient domain, image gradient histogram can be obtained. Then, by constructing a bimodal Gaussian function, the distribution of the gradient histogram can be restricted. Finally, by using the variational method to reconstruct the image from the gradient field, the contrast and target edges and details of the image are improved. The cutting of the amplitude of noise in the transform of gradient field prevents the amplification of noise. Based on actual experimental results, both in visual effects and quantitative indicators of the assessed value of the image information entropy, the algorithm is significantly better than the histogram equalization and specification. Therefore, it gives a good visual effect for the infrared image.
参考文献

[1] 程塨, 郭雷, 韩军伟, 等. 基于形态学带通滤波和尺度空间理论的红外弱小目标检测[J]. 光学学报, 2012, 32(10): 1015001.

    Cheng Gong, Guo Lei, Han Junwei, et al.. Infrared dim small target detection based on morphological band-pass filtering and scale space theory[J]. Acta Optica Sinica, 2012, 32(10): 1015001.

[2] 吴一全, 纪守新, 占必超. 基于无下采样轮廓波变换和独立分量分析的红外弱小目标检测[J]. 光学学报, 2011, 31(5): 0510002.

    Wu Yiquan, Ji Shouxin, Zhan Bichao. Infrared dim target detection based on nonsubsampled contourlet transform and independent component analysis[J]. Acta Optica Sinica, 2011, 31(5): 0510002.

[3] 王卫华, 李志军, 何艳, 等. 一种基于兴趣区提取的红外搜索系统目标实时检测算法[J]. 中国激光, 2012, 39(11): 1109001.

    Wang Weihua, Li Zhijun, He Yan, et al.. A real-time target detection algorithm for infrared search and track system based on region of interest extraction[J]. Chinese J Lasers, 2012, 39(11): 1109001.

[4] 万明, 张凤鸣, 胡双. 基于多步长梯度特征的红外弱小目标检测算法[J]. 光学学报, 2011, 31(10): 1011001.

    Wan Ming, Zhang Fengming, Hu Shuang. Novel infrared dim and small target detection algorithm based on multi-scale gradient[J]. Acta Optica Sinica, 2011, 31(10): 1011001.

[5] 黄康, 毛峡, 梁晓庚. 红外小目标图像的背景杂波量化方法[J]. 光学学报, 2011, 31(3): 0310001.

    Huang Kang, Mao Xia, Liang Xiaogeng. Background clutter quantification method for infrared image of small targets[J]. Acta Optica Sinica, 2011, 31(3): 0310001.

[6] Menotti D, Najman L, Facon J, et al.. Multi-histogram equalization methods for contrast enhancement and brightness preserving[J]. IEEE Transactions on Consumer Electronics, 2007, 53(3): 1186-1194.

[7] Wang Q, Ward R K. Fast image/video contrast enhancement based on weighted thresholded histogram equalization[J]. IEEE Transactions on Consumer Electronics, 2007, 53(2): 757-764.

[8] 胡正平, 刘博. 基于自适应直方图规定化函数引导的动态分层图像增强算法[J]. 燕山大学学报, 2009, 33(6): 471-477.

    Hu Zhengping, Liu Bo. Dynamic partition image enhancement algorithm induced by optimal adaptive histogram specification function[J]. Journal of Yanshan University, 2009, 33(6): 471-477.

[9] Sun C C, Ruan S J, Shie M C, et al.. Dynamic contrast enhancement based on histogram specification[J]. IEEE Transactions on Consumer Electronics, 2005, 51(4): 1300-1305.

[10] Iyad J, Hao Y. A new method for image contrast enhancement based on automatic specification of local histograms[J]. International Journal of Computer Science and Network Security, 2007, 7(7): 1-10.

[11] 朱立新, 王平安, 夏德深. 基于梯度场均衡化的图像对比度增强[J]. 计算机辅助设计与图形学学报, 2007, 19(12): 1546-1552.

    Zhu Lixin, Wang Ping′an, Xia Deshen. Image contrast enhancement by gradient field equalization[J]. J Computeraided Design & Computer Graphics, 2007, 19(12): 1546-1552.

[12] 费风长, 方志军, 曾卫明, 等. 基于区间映射规则的数字直方图处理 [J]. 计算机工程, 2006, 32(19): 217-220.

    Fei Fengchang, Fang Zhijun, Zeng Weiming et al.. Histogram processing of digital image based on interval mapping law[J]. Computer Engineering, 2006, 32(19): 217-220.

[13] 朱才志. 基于偏微分方程的数字图像处理的研究[D]. 安徽: 中国科学技术大学, 2007. 31-32.

    Zhu Caizhi. Research of Gigital Image Processing Based on Variational Partial Differential Equation[D]. Anhui: University of Science and Technology of China, 2007. 31-32.

[14] D A Soeolinsky. Dynamic range constraints in image fusion and visulization[C]. Proc Signal and Image Processing, IASTED, 2000.

[15] 韩希珍, 赵建. 结合偏微分方程增强图像纹理及对比度[J]. 光学 精密工程, 2012, 20(6): 1382-1388.

    Han Xizhen, Zhao Jian. Enhancement of image texture and contrast combined with partial differential equation[J]. Optics and Precision Engineering, 2012, 20(6): 1382-1388.

[16] 王超, 叶中付. 红外图像的变分增强算法[J]. 红外与毫米波学报, 2006, 25(4): 306-310.

    Wang Chao, Ye Zhongfu. Based on variational partial differential equation of infrared image enhancement[J]. Infrared & Millimeter Waves, 2006, 25(4): 306-310.

[17] 肖斌, 王暄, 毕秀丽, 等. 一种基于高斯函数的直方图规定化算法[J]. 铁道学报, 2006, 28(4): 119-122.

    Xiao Bin, Wang Xuan, Bi Xiuli, et al.. A histogram specification algorithm based on Gaussian PDF[J]. J China Railway Society, 2006, 28(4): 119-122.

赵文达, 赵建, 赵凡, 续志军. 双峰高斯函数规定化的变分红外图像增强[J]. 中国激光, 2014, 41(3): 0309002. Zhao Wenda, Zhao Jian, Zhao Fan, Xu Zhijun. Variable Infrared Image Enhancement of Bimodal Gaussian Function Specification[J]. Chinese Journal of Lasers, 2014, 41(3): 0309002.

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