Semantic Image Fusion

TL;DR

This paper introduces a novel semantic image fusion system that leverages pre-trained CNNs and class activation mappings to combine high-level semantic content from multiple images, achieving competitive low-level fusion performance.

Contribution

It presents a new framework for semantic image fusion using CNN feature maps and class activation mappings, enabling effective high-level content integration.

Findings

Achieves low-level fusion performance comparable to state-of-the-art methods.

Provides a flexible architecture for combining semantic information from multiple images.

Utilizes class activation mappings for high-level semantic fusion.

Abstract

Image fusion methods and metrics for their evaluation have conventionally used pixel-based or low-level features. However, for many applications, the aim of image fusion is to effectively combine the semantic content of the input images. This paper proposes a novel system for the semantic combination of visual content using pre-trained CNN network architectures. Our proposed semantic fusion is initiated through the fusion of the top layer feature map outputs (for each input image)through gradient updating of the fused image input (so-called image optimisation). Simple "choose maximum" and "local majority" filter based fusion rules are utilised for feature map fusion. This provides a simple method to combine layer outputs and thus a unique framework to fuse single-channel and colour images within a decomposition pre-trained for classification and therefore aligned with semantic fusion. Furthermore, class activation mappings of each input image are used to combine semantic information at a higher level. The developed methods are able to give equivalent low-level fusion performance to state of the art methods while providing a unique architecture to combine semantic information from multiple images.