Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Xin Feng,Kaiqun Hu,Xicheng Lou

doi:10.1109/access.2019.2962560

Abstract

The purpose of image fusion is to merge a substantial amount of information, such as contour, texture and intensity distribution information from original images, with a fusion image. To retain a considerable amount of the fusion image while retaining the texture details of the source image and maintaining the edge of the source image, this paper proposes an improved infrared and visible image fusion algorithm that is based on total variation. First, source infrared and visible light images and a difference image were decomposed by a total variation model, and their respective cartoon and texture components were acquired. A fitness function was solved according to the entropy, standard deviation and edge similarity of the infrared and visible light images. The optimal combination weight of various kinds of texture and cartoon components was sought via a wolf pack intelligence optimization algorithm to acquire the final fusion results with high-quality contrast ratio and edge details. The experimental results indicate that the proposed method not only can preserve edge contour information about the original image but also can effectively retain its texture detail information. The method is superior to the traditional multiscale and sparse representation fusion method with regard to various indicators, such as subjective visual effect, mutual information, gradient information, structural similarity and visual sensitivity.

Highlights

To satisfy actual application requirements, many valid methods are applied to imaging of the same scenario in different circumstances
The work is explained in 5 sections as follows: section two introduces the background of total variation decomposition and its steps in infrared and visible light image fusion; section three provides the weight optimization method of the wolf pack algorithm and the acquisition process of the fitness function; section four consists of an infrared and visible light image fusion experiment, analysis and discussion; and the final section provides the conclusions
The algorithm primarily consists of the following main steps: Infrared and visible light image fusion method based on total variation model and wolf pack algorithm

Summary

INTRODUCTION

To satisfy actual application requirements, many valid methods are applied to imaging of the same scenario in different circumstances. The final fusion result will likely contain more contour and texture information about the source image without artifacts or noise These traditional multiscale fusion methods disregarded different imaging characteristics between infrared images and visible light images, and the use of the same representation for their source images will generate a negative effect on the fusion image. The work is explained in 5 sections as follows: section two introduces the background of total variation decomposition and its steps in infrared and visible light image fusion; section three provides the weight optimization method of the wolf pack algorithm and the acquisition process of the fitness function; section four consists of an infrared and visible light image fusion experiment, analysis and discussion; and the final section provides the conclusions

TOTAL VARIATION MODEL DECOMPOSITION

EVALUATION METRICS

CONCLUSION