基于Retinex理论与概率非局部均值的红外图像增强方法

针对传统红外图像增强算法中细节模糊及过度增强的问题,提出了一种基于Retinex理论与概率非局部均值相结合的红外图像增强方法.首先通过单尺度Retinex方法调整图像中过暗与过亮部分的灰度级;然后利用概率非局部均值对图像进行分解处理得到基本层与细节层,对基本层采用直方图均衡化拉伸对比度,对细节层采用非线性函数进行增强;最后,将不同层次的结果融合得到对比度与细节增强的红外图像.用该方法对多组不同场景的红外图像进行仿真实验,并将其与多种增强方法进行主、客观对比分析,结果表明所提方法在红外图像的细节及对比度增强方面都获得了更好的效果.

Infrared Image Enhancement Based on Retinex and Probability Nonlocal Means Filtering

Aiming at the problem of over enhancement and low detailed in traditional image enhancement algorithm,an infrared image enhancement method based on Retinex theory and probability nonlocal mean is proposed.Firstly,the grayscale in deep dark and bright parts of image is adjusted by the single scale Retinex method.Then the image is decomposed into basic level and detail level by probability nonlocal mean filtering.For the basic layer,histogram equalization is used to stretch contrast,and the nonlinear function is used to enhance details for the detail layer.Finally,the different levels of the enhancement results are fused to obtain the infrared image with the contrast and detail enhanced.The simulated experiments on infrared images of different scenes are carried out through the proposed method.And the results are compared with those of different enhancement algorithms on the subjective and objective sides.The comparisons demonstrate that proposed method has better results in detail and contrast enhancement of infrared image.