激光与光电子学进展, 2015, 52 (1): 011001, 网络出版: 2014-12-29
基于人眼视觉特性的非下采样轮廓波变换域红外图像增强算法 下载: 626次
Infrared Image Enhancement Algorithm Based Human Visual System Characteristic Via Non-Subsampled Contourlet Transform Domain
图像处理 红外图像 图像增强 非下采样轮廓波变换 人眼视觉系统 非线性增益函数 image processing infrared image image enhancement non-subsampled contourlet transform human visual system nonlinear gain function
摘要
针对红外图像对比度低、噪声大等特点,提出了一种基于人类视觉系统的亮度掩蔽和对比度掩蔽特性的非下采样轮廓波变换(NSCT)域红外图像增强算法,在NSCT 域中定义一种带参数的对比度,对高频系数计算其带参数的对比度,用非线性增益函数对其进行增强,低对比度区域进行高增益,高对比度区域进行低增益,从而突出图像细节与提高图像对比度,同时通过估计噪声水平设置阈值,抑制绝对值小于阈值的系数,用以抑制噪声。对表示图像概貌的低频系数采用非完全贝塔函数进行非线性调整,从而提高图像的整体亮度。实验结果表明,该算法能够有效地对图像局部和整体进行增强,同时能够避免产生过增强现象,具有良好的视觉效果。
Abstract
Considering the low contrast, fuzzy edge of infrared images, an enhancement algorithm based on the luminance and contrast masking characteristics of the human visual system is presented, the parametric contrast is computed, then non-linear gain function is used to process the parametric contrast, which enhances low contrast more than high contrast, to improve image details and image contrast, then to suppress small coefficients by threshold denoising method. The incomplete beta function is applied to improve global brightness of image. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements. The phenomenon of over-enhancement is avoided. The enhanced image is of good visual perception.
朱国庆, 李庆武, 林少飞, 周亮基. 基于人眼视觉特性的非下采样轮廓波变换域红外图像增强算法[J]. 激光与光电子学进展, 2015, 52(1): 011001. Zhu Guoqing, Li Qingwu, Lin Shaofei, Zhou Liangji. Infrared Image Enhancement Algorithm Based Human Visual System Characteristic Via Non-Subsampled Contourlet Transform Domain[J]. Laser & Optoelectronics Progress, 2015, 52(1): 011001.