Scale-Invariant Structure Saliency Selection for Fast Image Fusion

TL;DR

This paper introduces a fast, scale-invariant image fusion method using difference-of-Gaussian pyramids, effectively preserving details across object sizes and enabling real-time high-resolution image fusion.

Contribution

It proposes a novel, efficient scale-invariant structure saliency selection scheme based on DoG pyramids for pixel-level image fusion.

Findings

Achieves competitive or superior visual quality and evaluation metrics.

Operates efficiently without complex computations.

Enables real-time high-resolution image fusion.

Abstract

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.