Image Fusion With Guided Filtering

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

I. Introduction

Image fusion is an important technique for various image processing and computer vision applications such as feature extraction and target recognition. Through image fusion, different images of the same scene can be combined into a single fused image [1]. The fused image can provide more comprehensive information about the scene which is more useful for human and machine perception. For instance, the performance of feature extraction algorithms can be improved by fusing multi-spectral remote sensing images [2]. The fusion of multi-exposure images can be used for digital photography [3]. In these applications, a good image fusion method has the following properties. First, it can preserve most of the useful information of different images. Second, it does not produce artifacts. Third, it is robust to imperfect conditions such as mis-registration and noise.