An Efficient Algorithm to Highlight Details in Infrared and Visible Image Fusion

Licai Zhang,Mingyang Li,Xiangrui Zhang,Peijin Liu

doi:10.1109/access.2021.3103111

Licai Zhang, Mingyang Li + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3103111

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 3	License type: CC BY 4.0

Affiliation: Xi'an University of Architecture and Technology

Abstract

To improve the fusion quality of infrared and visible images and highlight target and scene details, in this paper, a novel infrared and visible image fusion algorithm is proposed. First, a method for combining dynamic range compression and contrast restoration based on a guided filter is adopted to enhance the contrast of visible source images. Second, guided filter-based image multiscale decomposition is used to decompose images into base layers and detail layers. For base layer fusion, a fusion strategy based on the detail and energy measurements of the source image is proposed to determine the pixel value of the fused image base layer such that the energy loss of the fusion can be reduced and the texture detail features are highlighted to obtain more source image details. Finally, recursive separation and weighted histogram equalization methods are applied to optimize the fused image. Experimental results show that the fusion algorithm and fusion strategy proposed in this paper can effectively improve fusion image clarity, while more detailed target and scene information can still be retained.

Highlights

T HE fusion of infrared and visible images has been widely used in military, medical, remote sensing and many other fields in recent years [1]
EXPERIMENTS AND RESULTS ANALYSIS In the experiments, we use the dataset collected from the website http://www.imagefusion.org/, which has been widely used in image fusion testing [31]
Other classic or newly released fusion algorithms, including the fusion method based on nonsubsampled shear wave transform (NSST) [32], the fusion method based on guided filter (GF) multiscale decomposition (MGFF) [33], the fusion method based on infrared image structure extraction and visible image information retention (IFEVIP) [34],the multiscale fusion method through Gaussian and bilateral filters (HMSD) [35], the fusion method based on the convolutional neural network method (CNN) [36], the fusion method based on target-enhanced multiscale transform decomposition (TEMSD) [37], and the fusion method based on multiscale transformation and norm optimization (MST-NO) [38], are compared with the proposed method on some image samples

Summary

Introduction

T HE fusion of infrared and visible images has been widely used in military, medical, remote sensing and many other fields in recent years [1]. Infrared and visible light image fusion technology has been extensively studied by scholars [4]. Infrared sensors can perceive heat radiation of different wavelengths, which can capture hidden target contour information, and they have great night vision and fog penetration capabilities, but they cannot obtain detailed information and have poor resolution [6]. Visible light sensors characterize objects through spectral reflection, and the results have high resolution and rich background information, which are suitable for human visual perception, but the image quality is affected by the environment, especially at night and under low visibility conditions [3]. Infrared and visible image fusion technology is a research hotspot, and it plays a key role in target tracking and detection, face recognition and other fields [8], [9]

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Results

Conclusion