Scale-Invariant Structure Saliency Selection for Fast Image Fusion
This addresses the problem of efficient image fusion for applications requiring real-time processing of high-resolution images, though it is incremental as it builds on existing scale-space theory.
The paper tackles fast pixel-level image fusion by proposing a scale-invariant structure saliency selection method using a difference-of-Gaussian pyramid, resulting in competitive or better visual quality and objective metrics compared to state-of-the-art methods, with real-time capability for high-resolution images.
In this paper, we present a fast yet effective method for pixel-level scale-invariant image fusion in spatial domain based on the scale-space theory. Specifically, we propose a scale-invariant structure saliency selection scheme based on the difference-of-Gaussian (DoG) pyramid of images to build the weights or activity map. Due to the scale-invariant structure saliency selection, our method can keep both details of small size objects and the integrity information of large size objects in images. In addition, our method is very efficient since there are no complex operation involved and easy to be implemented and therefore can be used for fast high resolution images fusion. Experimental results demonstrate the proposed method yields competitive or even better results comparing to state-of-the-art image fusion methods both in terms of visual quality and objective evaluation metrics. Furthermore, the proposed method is very fast and can be used to fuse the high resolution images in real-time. Code is available at https://github.com/yiqingmy/Fusion.