Combining Regional Energy and Intuitionistic Fuzzy Sets for Infrared and Visible Image Fusion.

Xiaoxue Xing,Cong Luo,Cheng Liu,Jian Zhou,Minghan Yan,Tingfa Xu

doi:10.3390/s21237813

Abstract

To get more obvious target information and more texture features, a new fusion method for the infrared (IR) and visible (VIS) images combining regional energy (RE) and intuitionistic fuzzy sets (IFS) is proposed, and this method can be described by several steps as follows. Firstly, the IR and VIS images are decomposed into low- and high-frequency sub-bands by non-subsampled shearlet transform (NSST). Secondly, RE-based fusion rule is used to obtain the low-frequency pre-fusion image, which allows the important target information preserved in the resulting image. Based on the pre-fusion image, the IFS-based fusion rule is introduced to achieve the final low-frequency image, which enables more important texture information transferred to the resulting image. Thirdly, the ‘max-absolute’ fusion rule is adopted to fuse high-frequency sub-bands. Finally, the fused image is reconstructed by inverse NSST. The TNO and RoadScene datasets are used to evaluate the proposed method. The simulation results demonstrate that the fused images of the proposed method have more obvious targets, higher contrast, more plentiful detailed information, and local features. Qualitative and quantitative analysis results show that the presented method is superior to the other nine advanced fusion methods.

Highlights

Infrared (IR) and visible (VIS) images fusion focuses on synthesizing multiple images into one comprehensive image, which can be applied in face recognition [1], target detection [2], images enhancement [3], medicine field [4], remote sensing [5], and so on
In order to test the effectiveness of the proposed method, the experiments are conducted on two public datasets, TNO Image Fusion Dataset and RoadScene Dataset, which are widely used in the field of IR and VIS image fusion
The results show that our proposed method gets the best performance on 4 objective evaluation metrics (E, Average Gradient (AG), Spectral Distortion (SPD), Peak signal to noise ratio (PSNR)) and the second-best performance on 2 objective evaluation metrics (MI, Cross Entropy (CE))

Summary

Introduction

Infrared (IR) and visible (VIS) images fusion focuses on synthesizing multiple images into one comprehensive image, which can be applied in face recognition [1], target detection [2], images enhancement [3], medicine field [4], remote sensing [5], and so on. The source images applied in image fusion come from different sensors. The IR sensor can capture the heat information radiated by objects. IR images have a low spatial resolution, less background information, poor imaging performance, and high contrast pixel intensities. The VIS images provide abundant background, rich detailed texture information, and a high spatial resolution. The effective fusion of the two types of images will provide more useful information and better human visual effects, and that is beneficial for the subsequent research work [6,7]

Methods

Results

Conclusion