基于双通道PCNN的NSST域红外与可见光图像融合

针对传统红外与可见光图像融合中存在的一些不足,提出一种新的基于非下采样剪切波变换(NSST)和双通道脉冲耦合神经网络模型(2APCNN)的红外与可见光图像融合算法.该算法首先对红外图像进行预处理,提高源图像的对比度,再对红外与可见光图像进行NSST分解得到低频和高频子带系数;然后对分解后的低频子带系数进行二维小波分解再...

A Novel Infrared and Visible Image Fusion Algorithm Based on Dual-channel Pulse Coupled Neural Network in NSST Domain

This paper proposes a novel infrared and visible image fusion algorithm based on nonsubsampled shearlet transform( NSST) and dual-channel pulse coupled neural network( 2APCNN) in order to overcome the shortages of traditional image fusion algorithm. Firstly,an S-function is used to adaptively enhance the contrast of the infrared image. Secondly,the infrared and visible images are decomposed into low frequency and high frequency sub-bands by NSST transform. Thirdly,the low frequency and high frequency sub-bands of the infrared and visible images are obtained from the obtained low frequency sub-band by using wavelet transform( DWT).The significant figure rule is employed to fuse the acquired low frequency sub-bands,and the maximum method is projected to fuse the acquired high frequency sub-bands. Then,the low frequency sub-bands of NSST reconstruction are obtained by inversion DWT. Fourthly,the adaptive 2APCNN is projected to fuse the high frequency sub-bands in NSST domain. Finally,the fused image is obtained by performing the inverse NSST. The experiment results show that the proposed approach can obtain state-of-the-art performance compared with other image fusion methods in term of objective evaluation criteria and visual quality.