朱秋旭, 李俊山, 朱英宏, 李建华, 张姣. Retinex理论下的自适应红外图像增强[J]. 微电子学与计算机, 2013, 30(4): 22-25.
引用本文: 朱秋旭, 李俊山, 朱英宏, 李建华, 张姣. Retinex理论下的自适应红外图像增强[J]. 微电子学与计算机, 2013, 30(4): 22-25.
ZHU Qiu-xu, LI Jun-shan, ZHU Ying-hong, LI Jian-hua, ZHANG Jiao. Adaptive Infrared Thermal Image Enhancement Based on Retinex[J]. Microelectronics & Computer, 2013, 30(4): 22-25.
Citation: ZHU Qiu-xu, LI Jun-shan, ZHU Ying-hong, LI Jian-hua, ZHANG Jiao. Adaptive Infrared Thermal Image Enhancement Based on Retinex[J]. Microelectronics & Computer, 2013, 30(4): 22-25.

Retinex理论下的自适应红外图像增强

Adaptive Infrared Thermal Image Enhancement Based on Retinex

  • 摘要: 本文对Retinex的图像对比度增强方法进行修正,引入了非线性变换函数修正红外图像的照射分量和反射分量以及全局对比度增强函数拉伸图像照射分量,改善了全局图像视觉效果.同时使用非线性自适应S型函数进行局部灰度拉伸,从而更好地改善了图像对比度.提出的算法在增强红外图像细节,提高图像对比度方面优于已有的Retinex算法.该算法处理后的图像能够更有效地增强图像的对比度,突出图像的边缘与细节信息.

     

    Abstract: Aiming at the features of infrared image,a modified algorithm based on Retinex is proposed.A nonlinear transformation function is introduced to amend illumination and reflection components of infrared image.The global contrast enhancement function helps stretch illumination component which leads to improvement of the whole visual effect.Meanwhile the nonlinear sigmoid function is used to gray stretching locally so that better contrast of the image is achieved.Qualitative analysis and quantitative analysis are conducted by comparing the proposed algorithm and some classic Retinexalgorithms.It can be concluded from the comparison that proposed algorithm performs better than classic Retinex algorithms on enhancingtheinfrared image.the proposed algorithm can enhance contrast of infrared images and highlight the edge and details of the image effectively.

     

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