A Multi-Scale Infrared and Visible Image Fusion Network Based on Context Perception

Existing image fusion methods often fail to fully utilize the contextual information of the images, resulting in blurry and distorted fusion results. In addition, although the existing multi-scale feature extraction methods can consider the information of the source image from multiple perspectives, they tend to ignore the local information. These limitations degrade the performance and effectiveness of image fusion techniques in practical applications. To address above issues, we propose a fusion network architecture called MIA-Nest, consisting of encoder network, fusion network, and decoder network. Specifically, we introduce the Squeeze-and-Excitation Blocks(SEBs) into the encoder network, so that it can take local information into account while extracting multi-scale features. At the same time, in order to allow the fusion network to integrate the context information of the extracted features, we introduce Global Context Blocks (GCBs), so as to enrich the details in the fusion result and enable it to contain more information. Finally, the decoder network reconstructs the fusion image from the fused features obtained by the fusion network. Experimental results demonstrate that our proposed method significantly improves the quality and detail preservation capability of the fused images.