基于改进暗通道算法的红外图像去雾研究

为了提高红外图像去雾的质量,采用改进暗通道算法。首先通过图像自身调节来获得较准确的大气光强,利用原图暗原色图以及亮原色图的调节因子得到更加接近图像真实值的大气光强；然后均方差判断去雾图像区域块的对比度,通过透射率伸缩系数和透射率增减系数来调节透射率；接着为避免去雾图像画质偏暗,通过灰度值大小对图像亮度进行校正；最后给出了算法流程。实验仿真显示本文算法没有色彩失真现象,边缘细节处理更清晰明亮,平均梯度值最少为6.0,信息熵最少为7.5,峰值信噪比最少为17.6dB,能够满足红外图像去雾对清晰度、信息量的要求。

Infrared image dehazing based on improveddark channel prior algorithm

In order to improve the infrared image dehazing effect,using dark channel prior,an improved and fast method for single image dehazing is proposed.Firstly,atmospheric light intensity is obtained by image autoregulation,which is closer to the real value using the regulatory factors of dark and bright channel.Secondly,image dehazing region block contrast is judged using mean squared error,and transmission is adjusted using expansion and decrease coefficient.Thirdly,brightness is corrected using gray to avoid dim image.Finally,the algorithm flow is given.The experimental simulation shows that the color and edge detail information of IDCP has better visual effect than other algorithms,the average gradient is 6.0,the information entropy is 7.5,and the peak signal to noise ratio is 17.6 dB,which can satisfy the requirements of infrared image clearness and information.