基于NSST与DBM的可见光与红外图像融合方法

针对红外图像与可见光融合时特征信息无法充分提取的问题，提出了一种基于NSST与DBM的可见光与红外图像融合方法。该方法利用深度玻尔兹曼机（DBM）进行最优能量分割得到显著红外目标，并采用非下采样剪切波变换（NSST）将红外目标区域与背景区域融合的映射图进行稀疏分解和融合。仿真实验结果表明，与现有的几种经典方法相比，基于本文方法的结果图像拥有更好的视觉效果和更理想的客观结果。

Fusion Technique for Visible Light and Infrared Images based on Non-Subsampled Shearlet Transform and Deep Boltzmann Machine

Aiming at the problem of insufficient feature information extraction during the fusion course of visible light and infrared images, the fusion technique for visible light and infrared images based on non-subsampled shearlet transform (NSST) and deep Boltzmann machine (DBM) is proposed in this paper. It obtains the significant infrared targets after having optimal energy segmentation by making use of the deep Boltzmann machine (DBM), as well as puts the mapping graph combined the infrared targets region with the background region into sparse decomposition and fusion by adopting the non-downsampled shear wave transform (NSST) . The simulation results show that the results based on the proposed method have better visual effect and better objective performance compared with the existing classical methods.