Fast and Efficient Zero-Learning Image Fusion
This work addresses the need for efficient image fusion in applications like thermal, medical, and multi-focus imaging, but it is incremental as it builds on existing decomposition and pre-trained network techniques.
The authors tackled the problem of real-time image fusion from multiple sources by proposing a method that decomposes images into base and detail layers, fusing them using visual saliency and pre-trained neural network features, achieving state-of-the-art performance in visual quality, objective assessment, and runtime efficiency.
We propose a real-time image fusion method using pre-trained neural networks. Our method generates a single image containing features from multiple sources. We first decompose images into a base layer representing large scale intensity variations, and a detail layer containing small scale changes. We use visual saliency to fuse the base layers, and deep feature maps extracted from a pre-trained neural network to fuse the detail layers. We conduct ablation studies to analyze our method's parameters such as decomposition filters, weight construction methods, and network depth and architecture. Then, we validate its effectiveness and speed on thermal, medical, and multi-focus fusion. We also apply it to multiple image inputs such as multi-exposure sequences. The experimental results demonstrate that our technique achieves state-of-the-art performance in visual quality, objective assessment, and runtime efficiency.