2019/05/09 by Fayez Lahoud, Lahoud, Fayez, Sabine Süsstrunk +1 · 1 citation
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Photoacoustic and Ultrasonic Imaging #cs.CV
paper · pdf · doi:10.48550/arxiv.1905.03590
13 pages, 10 figures
arxiv created 2019/05/09 · openalex publication_date 2019/05/09 · arxiv updated 2019/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.