Application of Deep Boltzmann Machine to Image Fusion
Journal Title: 河南科技大学学报（自然科学版） - Year 2018, Vol 39, Issue 1
Abstract
Aiming at the problem of insufficient feature information extraction during the fusion course of visible light and infrared images, the fusion method for visible light and infrared images based on non-subsampled shearlet transform (NSST) and deep Boltzmann machine (DBM) was proposed.First, optimal energy segmentation on the infrared image was conducted via DBM to extract the significant infrared target.Secondly, the regions of significant targets and backgrounds via segmentations were projected to the source images respectively to obtain corresponding maps.Finally, the multi-scale and multi-direction sparse decompositions of visible light and infrared projection images were performed by NSST, respectively.A series of sub-images of the source images were fused.The final fused image can be obtained by inverse NSST.The results of the simulation experiment show that compared with three common methods, the method discussed in the paper is higher than contourlet transform (CT) 4.1% in information entropy.It's better than NSST about 10% in average gradient, and it is higher than non-subsampled contourlet transform (NSCT) about 3.8% in mutual information.The fusion images acquired from the method discussed in the paper has a higher contrast degree and more specific information.
Authors and Affiliations
Fengnian LIU
Keywords
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