A Two-To-One Deep Learning General Framework for Image Fusion.

ABSTRACT: The image fusion algorithm has great application value in the domain of computer vision, which makes the fused image have a more comprehensive and clearer description of the scene, and is beneficial to human eye recognition and automatic mechanical detection. In recent years, image fusion algorithms have achieved great success in different domains. However, it still has huge challenges in terms of the generalization of multi-modal image fusion. In reaction to this problem, this paper proposes a general image fusion framework based on an improved convolutional neural network. Firstly, the feature information of the input image is captured by the multiple feature extraction layers, and then multiple feature maps are stacked along the number of channels to acquire the feature fusion map. Fina

SUBMITTER: Zhu P 

PROVIDER: S-EPMC9376963 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature