针对高动态红外图像位压缩和细节增强过程中的噪声放大、微小细节增强不足以及强边缘过度增强等问题,提出一种基于双导向滤波的细节增强与去噪算法.用导向滤波分别获得两组基图和细节图,低ε 参数基图作为去噪基图的估计;低ε 参数与高ε 参数细节图之差作为去噪细节图的估计;两图分别经过自动增益控制和位压缩后,合成为增强去噪图像.为准确估计参数,提出一种基于细节图像素灰度值变化规律统计的优化模型,分类考察像素灰度值收敛特性后给出参数取值范围.仿真结果表明,该算法能够准确选择关键参数,在增强细节和抑制噪声的同时,平衡微小细节和强边缘增强效果,并具有准实时性、模型简单和控制参数较少等特点.%Focusing on the noise amplification, insufficient enhancement of small details and excessive enhancement of edge in the process of high dynamic range infrared image compression and detail enhancement, a novel detail enhancement and denoising method based on double guided image filtering is proposed in the present study. We first applied GIF to obtain two groups of base images and detail images. The base image with low ε is used as an estimate of denoised base component and correspondingly the detail image as the noise estimation. Thus, the difference between the two detail images can be logically used to estimate the denoised detail. After the two estimated components are processed and compressed into the display range by our modified automatic gain control method respectively, we recombine the two parts and obtain the enhanced and denoised image. An optimization model based on classification statistics of gray value convergency in detail pixels is also presented, which provides reasonable numerical range of the critical parameter ε in GIF. The experimental evaluation shows that the algorithm can accurately choose key parameters and improve the slight details and enhance edges while highlighting details and reducing noises. Furthermore, our proposed method is characteristic of being real-time, requiring simpler models and fewer parameters.
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