Unsupervised fusion method of infrared and visible images based on dense self-attention
Image fusion plays a pivotal role in the field of computer vision,offering comprehensive data support by integrating information from various modalities or sensors.It finds wide application in sectors such as autonomous driving and military operations.Nevertheless,current fusion methods relying on convolutional neural networks encounter challenges in establishing sufficient global dependencies,particularly in intricate scenarios,which consequently results in an inadequate perceptual quality of the fused images,thereby constraining the fusion performance of infrared and visible images.In addressing this issue,this paper leverages the self-attention mechanism to establish global dependencies within the images and proposes a dense self-attention-based fusion method for infrared and visible images.Initially,a module for deep feature extraction is devised,ensuring a reduced number of network parameters while proficiently extracting multi-scale features from infrared and visible images.Subsequently,the two-branch Transformer module is integrated with the dense self-attention module to refine the global self-attention weight matrix of the images,converting the feature weight matrix into a dense self-attention matrix to facilitate more effective learning of global feature relations.Ultimately,features of varying scales are merged,and the ultimate fusion outcome is achieved through feature reconstruction.Experimental findings demonstrate that,in comparison to nine other fusion algorithms,the proposed method adeptly integrates deep detail features of the image and exhibits noticeable advantages in objective metrics.
万方数据
维普
