基于快速交替引导滤波和CNN的红外与可见光图像融合

Infrared and visible image fusion based on fast alternating guided filtering and CNN

为了解决红外与可见光图像融合中出现细节信息丢失,边缘模糊以及伪影的问题,本文提出一种快速交替引导滤波,在保证融合图像质量的前提下有效提高运行效率,结合CNN(卷积神经网络)以及红外特征提取进行有效的融合。首先,对源图像利用四叉树分解和贝塞尔插值来提取红外亮度特征结合可见光图像得到初始融合图像。其次,通过快速交替引导滤波获取源图像的基础层与细节层信息,基础层通过CNN与拉普拉斯变换得到融合后的基础图像,细节层通过显著性测量的方法得到融合后的细节图像。最后,将初始融合图、基础融合图以及细节融合图进行相加得到最终融合结果。本算法涉及到的快速交替引导滤波以及特征提取性能使得最终融合结果中包含丰富的纹理细节信息,边缘清晰。经实验表明,本算法所得融合结果在视觉方面具有较好的保真度,客观评价指标较对比方法其信息熵、标准差、空间频率、小波特征互信息、视觉保真度以及平均梯度分别平均提高了9.9%,6.8%,43.6%,11.3%,32.3%,47.1%。

In order to solve the problems of the loss of detail information,blurred edges,and artifacts in infrared and visible image fusion,this paper proposes a fast alternating guided filter,which significantly increases the operation efficiency while ensuring the quality of the fused image.The proposed filer combines a convolutional neural network(CNN)and infrared feature extraction effective fusion.First,quadtree decomposition and Bessel interpolation are used to extract the infrared brightness features of the source images,and the initial fusion image is obtained by combining the visible image.Second,the information of the base layer and the detail layer of the source images is obtained through fast alternating guided filtering.The base layer obtains the fused base image through the CNN and Laplace transform,and the detail layer obtains the fused detail image through the saliency measurement method.Finally,the initial fusion map,basic fusion map,and detail fusion map are added to obtain the final fusion result.Because of the fast alternating guided filtering and feature extraction performance of this algorithm,the final fusion result contains rich texture details and clear edges.The experimental results indicate that the fusion results obtained by the algorithm have good fidelity in vision,and its objective evaluation indicators are compared with those of other methods.The information entropy,standard deviation,spatial frequency,wavelet feature mutual information,visual fidelity,and average gradient show improvements by 9.9%,6.8%,43.6%,11.3%,32.3%,and 47.1%,respectively,on average.