Infrared and Visible Image Fusion via Fast Approximate Bilateral Filter and Local Energy Characteristics

Abstract

Image fusion is to effectively enhance the accuracy, stability, and comprehensiveness of information. Generally, infrared images lack enough background details to provide an accurate description of the target scene, while visible images are difficult to detect radiation under adverse conditions, such as low light. People hoped that the richness of image details can be improved by using effective fusion algorithms. In this paper, we propose an infrared and visible image fusion algorithm, aiming to overcome some common defects in the process of image fusion. Firstly, we use fast approximate bilateral filter to decompose the infrared image and visible image to obtain the small-scale layers, large-scale layer, and base layer. Then, the fused base layer is obtained based on local energy characteristics, which avoid information loss of traditional fusion rules. The fused small-scale layers are acquired by selecting the absolute maximum, and the fused large-scale layer is obtained by summation rule. Finally, the fused small-scale layers, large-scale layer, and base layer are merged to reconstruct the final fused image. Experimental results show that our method retains more detailed appearance information of the fused image and achieves good results in both qualitative and quantitative evaluations.

An Infrared and Visible Image Fusion Algorithm Method Based on a Dual Bilateral Least Squares Hybrid Filter

“…Singh et al [15] proposed an image-fusion method based on a discrete wavelet transform and a bilateral filter to better preserve the edges of the image, but it reduces the contrast of the infrared image in the fused image. Li et al [16] proposed an image-fusion algorithm based on a fast approximate bilateral filter and local energy features, in which the algorithm utilizes a fast approximate bilateral filter to decompose the source image five times, obtaining a base layer and several detail layer images. However, the algorithm preserves edge features more efficiently at the expense of time and reduces halos.…”