Multi-focus Image Fusion Method Based on PCANet in NSST Domain

The deep learning model based image fusion methods have attracted much attention in recently years.But the traditio-nal deep learning model usually needs a time-consuming and complex training process and a difficulty parameters tuning process on large datasets.To overcome these problems,a simple deep learning model PCANet based multi-focus image fusion method in NSST domain is proposed.Firstly,multi-focus images are used to train two-stage PCANet to extract image features.Then,the input source image is decomposed by NSST to obtain the multi-scale and multi-directional representation of the source image.The low frequency subband uses the trained PCANet to extract its image features,and uses the kernel norm to construct an effective feature space for image fusion.High frequency subbands are fused using the fusion rule of regional energy maximization.Finally,the frequency coefficients fused according to different fusion rules are reconstructed by NSST to obtain a clear target image.The experimental results show that the training and fusion speed of the algorithm is 43% higher than that of the CNN-based method.The average gradient,spatial frequency and entropy of the proposed algorithm are 5.744,15.560 and 7.059 respectively,which can be comparable to or superior to the existing fusion methods